Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
This document describes a bee hive status control system that uses sensors and wireless communication to monitor temperature and humidity in bee hives located in different areas. The system uses Arduino boards with sensors in each hive to collect temperature and humidity data, which is sent via radio frequency to a central system. The central system then sends SMS messages with the sensor readings to keep beekeepers informed. The system aims to help nomadic beekeeping be more efficient by allowing remote monitoring of hive conditions.
An assessment of stingless beehive climate impact using multivariate recurren...IJECEIAES
A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise shortterm forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long shortterm memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.
Optimizing olive disease classification through transfer learning with unmann...IJECEIAES
Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks.
This document describes the design of a low-cost smart egg incubator. It uses sensors to monitor temperature and humidity inside the incubator. A microcontroller coordinates the incubator components like heaters, fans, and an egg turning mechanism to maintain optimal conditions for hatching. A mobile app allows farmers to monitor the incubator remotely. The goal is to increase egg hatchability rates in a cost-effective design suitable for local Ghanaian poultry farmers.
This document describes a proposed smart poultry farm control system using Internet of Things (IoT) technologies. Sensors would monitor environmental parameters like temperature, humidity, and heat in the farm. An Arduino microcontroller would connect the sensors to a cloud database and control fans and lights automatically based on the sensor readings. Chicken details like weight and purchase date would also be stored in the database. The system aims to automate farm monitoring and control to improve productivity and quality while reducing costs and labor.
Analysis and prediction of seed quality using machine learning IJECEIAES
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Peanut leaf spot disease identification using pre-trained deep convolutional...IJECEIAES
Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
Assessing the advancement of artificial intelligence and drones’ integration ...IJECEIAES
Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.
This document describes a bee hive status control system that uses sensors and wireless communication to monitor temperature and humidity in bee hives located in different areas. The system uses Arduino boards with sensors in each hive to collect temperature and humidity data, which is sent via radio frequency to a central system. The central system then sends SMS messages with the sensor readings to keep beekeepers informed. The system aims to help nomadic beekeeping be more efficient by allowing remote monitoring of hive conditions.
An assessment of stingless beehive climate impact using multivariate recurren...IJECEIAES
A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise shortterm forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long shortterm memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.
Optimizing olive disease classification through transfer learning with unmann...IJECEIAES
Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks.
This document describes the design of a low-cost smart egg incubator. It uses sensors to monitor temperature and humidity inside the incubator. A microcontroller coordinates the incubator components like heaters, fans, and an egg turning mechanism to maintain optimal conditions for hatching. A mobile app allows farmers to monitor the incubator remotely. The goal is to increase egg hatchability rates in a cost-effective design suitable for local Ghanaian poultry farmers.
This document describes a proposed smart poultry farm control system using Internet of Things (IoT) technologies. Sensors would monitor environmental parameters like temperature, humidity, and heat in the farm. An Arduino microcontroller would connect the sensors to a cloud database and control fans and lights automatically based on the sensor readings. Chicken details like weight and purchase date would also be stored in the database. The system aims to automate farm monitoring and control to improve productivity and quality while reducing costs and labor.
Analysis and prediction of seed quality using machine learning IJECEIAES
The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.
Peanut leaf spot disease identification using pre-trained deep convolutional...IJECEIAES
Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
Assessing the advancement of artificial intelligence and drones’ integration ...IJECEIAES
Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.
This document lays out a project plan for the development of a "Smart Poultry Farming System" an automated Poultry Farm that helps the farmers to keep a daily track of the activities going on in his her farm remotely through use of the Internet. By using technologies like IOT and WSN, the amount of temperature, Humidity, gases in air, Luminosity and water level of the poultry can be taken care of. Nikhil Zutshi | Monika | Pratik Yadav | Prabhdeep Singh Basra "Animal Husbandry Farm Automation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31203.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31203/animal-husbandry-farm-automation/nikhil-zutshi
This document summarizes a seminar topic on animal monitoring using the Internet of Things. It discusses using sensors on animal collars to monitor body temperature, rumination, heart rate, and location. The hardware architecture uses collars on animals to collect sensor data and beacons to track location. A cloud platform analyzes the collected data to provide information to farmers on animal behavior and health. Potential applications include early detection of health issues in animals and understanding animal movement patterns. Future areas of research may involve using satellite tracking and camera traps to monitor wild animals. The conclusion evaluates machine learning algorithms for categorizing animal posture data collected from sensor collars.
The document summarizes a training program on sustainable rural development held in South Korea from September 27th to October 8th 2021. It then provides an overview of the evolution of agricultural technology from early tools to modern smart farming techniques using IoT, AI, and blockchain. It discusses how these technologies are being applied to monitor crops and livestock, predict yields, and ensure traceability. Going forward, it mentions initiatives like the Agritech Challenge to promote innovations that address issues for smallholder farmers around productivity, climate change and supply chains.
David Kinnear: Can Tech Save The Honeybees?David Kinnear
In this presentation, David Kinnear discusses the decline of honeybees and how recent technological advancements promise to alleviate issues related to that decline.
Internet of Things Hydroponics Agriculture (IoTHA) Using Web/Mobile ApplicationsIRJET Journal
This document discusses using Internet of Things (IoT) technology to develop an automated hydroponics system using web and mobile applications. The system would monitor and control nutritional and environmental parameters to optimize plant growth without soil. Specifically, it details a study that developed an IoT-driven hydroponics testbed to cultivate tomatoes and collect data on their nutrient requirements and productivity. The results showed the plants grew well based on computer vision and visual analysis. Adopting this type of hydroponics farming using IoT could help address limited agricultural land and increase production capacity by allowing year-round farming.
Today, majority of the farmers are dependent on agriculture for their survival. But
majority of the agricultural tools and practices are outdated and it yields less crop
products, because everything is depends on environment and Government support. The
world population is becoming more comparatively cultivation land and crop yield. It is
essential for the world to increase the yielding of the crop by adopting information
technology and communication plays a vital role in smart farming. The objective of this
research paper to present tools and best practices for understanding the role of
information and communication technologies in agriculture sector, motivate and make
the illiterate farmers to understand the best insights given by the big data analytics using
machine learning
IoT Based Intelligent Management System for Agricultural Applicationijtsrd
The growth of technology in any sector is not there in agriculture and this is a problem for India. The government has struggled to do anything for the farming sector which is in an exceptionally deplorable state. The pause in decision making also has led to Indias high rate of unemployment owing to the quality of the economy. The applications in well developed countries involve robotics, aircraft, and artificial intelligence, but they can raise the cost of running and sustain. Currently, operating drones such as these is difficult. In India, only a few farmers can afford to employ such high tech machinery to farm owing to financial constraints. The project is aimed at developing an affordable quad copter for farmers to use on their crops, with the goal of growing their output. We are developing core a framework with support of Raspberry Pi and OpenCV that can help predict crops yield with the help of inputs from numerous different sensor packages. Venkat. P. Patil | Umakant B. Gohatre | Anushka Mhaskar | Akash Jadhav | Prajwal Shetty | Yash Jadhav "IoT Based Intelligent Management System for Agricultural Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38578.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/38578/iot-based-intelligent-management-system-for-agricultural-application/venkat-p-patil
Detection of diseases in rice leaf using convolutional neural network with tr...IJECEIAES
Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
KPI Deployment for Enhanced Rice Production in a Geo-Location Environment us...ijwmn
Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
KPI DEPLOYMENT FOR ENHANCED RICE PRODUCTION IN A GEO-LOCATION ENVIRONMENT USI...ijwmn
Rice production plays a significant role in food security in the globe. The automation of rice production
remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in
consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for
enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria.
The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer,
weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed
constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various
network scenarios to demonstrate data sensing of different environmental variables in a given farm land.
This was achieved by varying network devices at different scenarios using OPNET simulator and
understudying the network performances. Each new set of network devices was integrated to a Zigbee
Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus
representing data sensing of a particular environmental variable. Three different scenarios were designed
and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors
generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The
temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient
Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective
communication between different network components and provides insight on how WSN could be used
simultaneously to monitor a number of different environmental variables on a farm field. By implementing
the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare
of a rice farm.
Bioinformatics Core at AGERI Present and FutureRABNENA Network
The Agricultural Genetic Engineering Research Institute (AGERI) in Egypt was established in 1989 as a vehicle for applying genetic engineering in agriculture. It has since developed a bioinformatics core to help analyze complex biological data using various software and hardware, including servers, cluster computers, and analysis tools. Going forward, AGERI aims to expand its bioinformatics department by obtaining additional cluster computers and software for processing the large amounts of data being generated through techniques like next-generation sequencing.
IRJET- Review Paper on –Baby Cradle Monitoring SystemIRJET Journal
This document describes a proposed baby cradle monitoring system that uses various sensors to monitor babies and update parents. It discusses designing a smart baby cradle with features like camera monitoring, automatic swinging when the baby cries, sensing wetness in the baby's bed, monitoring the baby's presence in the cradle, and sending SMS messages to parents' phones to notify them about the baby's status. The proposed system aims to help busy working parents remotely monitor their babies using new technologies while alleviating their stress and anxiety. It outlines the existing limitations and proposes a new architecture using sensors, microcontrollers, and wireless communication.
Animal Repellent System for Smart Farming Using AI and Deep LearningIRJET Journal
This document summarizes a research paper on developing an animal repellent system for smart farming using artificial intelligence and deep learning. The system uses a camera to collect animal data, which is then classified using a deep convolutional neural network model trained on the data. It can identify different animal species in real-time. When an animal is detected, it sends an alert message and produces the appropriate ultrasonic frequency to repel that species. Testing on an animal dataset showed the CNN achieved over 98% accuracy in identifying animals. The system provides a real-time monitoring solution using AI to help farmers prevent crop damage from animals.
This document presents a system that uses IoT and machine learning techniques to detect crop health through data collected by UAVs. The system was able to detect crop health with various machine learning models and evaluate performance metrics like recall, precision and f-measure. However, accurately detecting crop health remains challenging and depends on additional crop and environmental factors. Future work aims to improve data sharing and analysis of more detailed metrics to better generate recommendations.
Automation techniques have been increasingly used in livestock production to reduce labor needs. This includes automatic identification of animals using RFID tags, GPS tracking, or retinal/muzzle scanning. Other automated processes discussed are feeding, milking, estrus detection through activity/hormone monitoring, birth detection, online herd management, and barn cleaning/environment control. The document concludes that while automation increases production and efficiency, it also increases costs, so is best for large commercial farms.
Poonsri Vate-U-Lan, Donna Quigley, Panicos Masouras: Internet of things in ag...Dr Poonsri Vate-U-Lan
The objective of this paper is to report a case study of smart dairy farming in Ontario, Canada which is the future of food production and ways that advancements related to the Internet of Things (IoT). It is impacting upon agricultural practice in the form of smart farming. Smart farming is the practice of intelligent agricultural management based upon technological data gathering farm practice for the purpose of increased levels of quality, production, and environmental protection. This paper will illustrate one example whereby partnerships among the academic world, government agencies and local food producing communities in Canada are adapting innovative thinking and smart technologies to address the need to implement the more effective agricultural practice. Food From Thought is a Canadian research project, based upon high-tech information systems to produce enough food for a growing human population while sustaining the Earth’s ecosystems. The paper will outline how one dairy farmer in Ontario has been able to apply smart farming technologies to increase milk production while maintaining the health of his cattle and preserving the environment. The review of applications of smart farming in Ontario such as digital tracking for a cow, genomic testing, digitally signaled birth, sensor driven crop management and data driven dairy production also details in this article.
IRJET - Farm Field Monitoring Using IoT-A Survey PaperIRJET Journal
This document discusses using IoT technology to monitor farm fields. It reviews several existing research papers on IoT-based farm monitoring systems that use sensors to measure soil moisture, temperature, humidity and other factors. However, many of these systems have limitations such as high costs, lack of scalability, and sensors that cannot withstand harsh agricultural field conditions. The document proposes a new IoT farm monitoring system with sensors connected to a microcontroller and cloud platform. This would allow farmers to monitor field conditions remotely using a mobile app or LCD display and receive notifications. The system aims to help farmers increase crop yields while reducing water waste and crop losses.
How Artificial Intelligence, IoT and Big Data Can Save The BeesBernard Marr
Artificial intelligence (AI), internet of things sensors, big data analytics, and other technologies are being used to monitor and develop solutions to mitigate the global issue of declining honey bee and pollinator populations. The World Bee Project and Oracle are using technology to learn more about how to save the bees.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Similaire à A review on internet of things-based stingless bee's honey production with image detection framework
This document lays out a project plan for the development of a "Smart Poultry Farming System" an automated Poultry Farm that helps the farmers to keep a daily track of the activities going on in his her farm remotely through use of the Internet. By using technologies like IOT and WSN, the amount of temperature, Humidity, gases in air, Luminosity and water level of the poultry can be taken care of. Nikhil Zutshi | Monika | Pratik Yadav | Prabhdeep Singh Basra "Animal Husbandry Farm Automation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31203.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31203/animal-husbandry-farm-automation/nikhil-zutshi
This document summarizes a seminar topic on animal monitoring using the Internet of Things. It discusses using sensors on animal collars to monitor body temperature, rumination, heart rate, and location. The hardware architecture uses collars on animals to collect sensor data and beacons to track location. A cloud platform analyzes the collected data to provide information to farmers on animal behavior and health. Potential applications include early detection of health issues in animals and understanding animal movement patterns. Future areas of research may involve using satellite tracking and camera traps to monitor wild animals. The conclusion evaluates machine learning algorithms for categorizing animal posture data collected from sensor collars.
The document summarizes a training program on sustainable rural development held in South Korea from September 27th to October 8th 2021. It then provides an overview of the evolution of agricultural technology from early tools to modern smart farming techniques using IoT, AI, and blockchain. It discusses how these technologies are being applied to monitor crops and livestock, predict yields, and ensure traceability. Going forward, it mentions initiatives like the Agritech Challenge to promote innovations that address issues for smallholder farmers around productivity, climate change and supply chains.
David Kinnear: Can Tech Save The Honeybees?David Kinnear
In this presentation, David Kinnear discusses the decline of honeybees and how recent technological advancements promise to alleviate issues related to that decline.
Internet of Things Hydroponics Agriculture (IoTHA) Using Web/Mobile ApplicationsIRJET Journal
This document discusses using Internet of Things (IoT) technology to develop an automated hydroponics system using web and mobile applications. The system would monitor and control nutritional and environmental parameters to optimize plant growth without soil. Specifically, it details a study that developed an IoT-driven hydroponics testbed to cultivate tomatoes and collect data on their nutrient requirements and productivity. The results showed the plants grew well based on computer vision and visual analysis. Adopting this type of hydroponics farming using IoT could help address limited agricultural land and increase production capacity by allowing year-round farming.
Today, majority of the farmers are dependent on agriculture for their survival. But
majority of the agricultural tools and practices are outdated and it yields less crop
products, because everything is depends on environment and Government support. The
world population is becoming more comparatively cultivation land and crop yield. It is
essential for the world to increase the yielding of the crop by adopting information
technology and communication plays a vital role in smart farming. The objective of this
research paper to present tools and best practices for understanding the role of
information and communication technologies in agriculture sector, motivate and make
the illiterate farmers to understand the best insights given by the big data analytics using
machine learning
IoT Based Intelligent Management System for Agricultural Applicationijtsrd
The growth of technology in any sector is not there in agriculture and this is a problem for India. The government has struggled to do anything for the farming sector which is in an exceptionally deplorable state. The pause in decision making also has led to Indias high rate of unemployment owing to the quality of the economy. The applications in well developed countries involve robotics, aircraft, and artificial intelligence, but they can raise the cost of running and sustain. Currently, operating drones such as these is difficult. In India, only a few farmers can afford to employ such high tech machinery to farm owing to financial constraints. The project is aimed at developing an affordable quad copter for farmers to use on their crops, with the goal of growing their output. We are developing core a framework with support of Raspberry Pi and OpenCV that can help predict crops yield with the help of inputs from numerous different sensor packages. Venkat. P. Patil | Umakant B. Gohatre | Anushka Mhaskar | Akash Jadhav | Prajwal Shetty | Yash Jadhav "IoT Based Intelligent Management System for Agricultural Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38578.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/38578/iot-based-intelligent-management-system-for-agricultural-application/venkat-p-patil
Detection of diseases in rice leaf using convolutional neural network with tr...IJECEIAES
Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
EFRA is an EU-funded project that will develop the first analytics-enabled, green data space for AI-enabled food risk prevention. It will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat.
KPI Deployment for Enhanced Rice Production in a Geo-Location Environment us...ijwmn
Rice production plays a significant role in food security in the globe. The automation of rice production remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria. The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer, weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various network scenarios to demonstrate data sensing of different environmental variables in a given farm land. This was achieved by varying network devices at different scenarios using OPNET simulator and understudying the network performances. Each new set of network devices was integrated to a Zigbee Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus representing data sensing of a particular environmental variable. Three different scenarios were designed and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective communication between different network components and provides insight on how WSN could be used simultaneously to monitor a number of different environmental variables on a farm field. By implementing the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare of a rice farm.
KPI DEPLOYMENT FOR ENHANCED RICE PRODUCTION IN A GEO-LOCATION ENVIRONMENT USI...ijwmn
Rice production plays a significant role in food security in the globe. The automation of rice production
remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in
consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for
enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria.
The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer,
weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed
constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various
network scenarios to demonstrate data sensing of different environmental variables in a given farm land.
This was achieved by varying network devices at different scenarios using OPNET simulator and
understudying the network performances. Each new set of network devices was integrated to a Zigbee
Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus
representing data sensing of a particular environmental variable. Three different scenarios were designed
and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors
generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The
temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient
Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective
communication between different network components and provides insight on how WSN could be used
simultaneously to monitor a number of different environmental variables on a farm field. By implementing
the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare
of a rice farm.
Bioinformatics Core at AGERI Present and FutureRABNENA Network
The Agricultural Genetic Engineering Research Institute (AGERI) in Egypt was established in 1989 as a vehicle for applying genetic engineering in agriculture. It has since developed a bioinformatics core to help analyze complex biological data using various software and hardware, including servers, cluster computers, and analysis tools. Going forward, AGERI aims to expand its bioinformatics department by obtaining additional cluster computers and software for processing the large amounts of data being generated through techniques like next-generation sequencing.
IRJET- Review Paper on –Baby Cradle Monitoring SystemIRJET Journal
This document describes a proposed baby cradle monitoring system that uses various sensors to monitor babies and update parents. It discusses designing a smart baby cradle with features like camera monitoring, automatic swinging when the baby cries, sensing wetness in the baby's bed, monitoring the baby's presence in the cradle, and sending SMS messages to parents' phones to notify them about the baby's status. The proposed system aims to help busy working parents remotely monitor their babies using new technologies while alleviating their stress and anxiety. It outlines the existing limitations and proposes a new architecture using sensors, microcontrollers, and wireless communication.
Animal Repellent System for Smart Farming Using AI and Deep LearningIRJET Journal
This document summarizes a research paper on developing an animal repellent system for smart farming using artificial intelligence and deep learning. The system uses a camera to collect animal data, which is then classified using a deep convolutional neural network model trained on the data. It can identify different animal species in real-time. When an animal is detected, it sends an alert message and produces the appropriate ultrasonic frequency to repel that species. Testing on an animal dataset showed the CNN achieved over 98% accuracy in identifying animals. The system provides a real-time monitoring solution using AI to help farmers prevent crop damage from animals.
This document presents a system that uses IoT and machine learning techniques to detect crop health through data collected by UAVs. The system was able to detect crop health with various machine learning models and evaluate performance metrics like recall, precision and f-measure. However, accurately detecting crop health remains challenging and depends on additional crop and environmental factors. Future work aims to improve data sharing and analysis of more detailed metrics to better generate recommendations.
Automation techniques have been increasingly used in livestock production to reduce labor needs. This includes automatic identification of animals using RFID tags, GPS tracking, or retinal/muzzle scanning. Other automated processes discussed are feeding, milking, estrus detection through activity/hormone monitoring, birth detection, online herd management, and barn cleaning/environment control. The document concludes that while automation increases production and efficiency, it also increases costs, so is best for large commercial farms.
Poonsri Vate-U-Lan, Donna Quigley, Panicos Masouras: Internet of things in ag...Dr Poonsri Vate-U-Lan
The objective of this paper is to report a case study of smart dairy farming in Ontario, Canada which is the future of food production and ways that advancements related to the Internet of Things (IoT). It is impacting upon agricultural practice in the form of smart farming. Smart farming is the practice of intelligent agricultural management based upon technological data gathering farm practice for the purpose of increased levels of quality, production, and environmental protection. This paper will illustrate one example whereby partnerships among the academic world, government agencies and local food producing communities in Canada are adapting innovative thinking and smart technologies to address the need to implement the more effective agricultural practice. Food From Thought is a Canadian research project, based upon high-tech information systems to produce enough food for a growing human population while sustaining the Earth’s ecosystems. The paper will outline how one dairy farmer in Ontario has been able to apply smart farming technologies to increase milk production while maintaining the health of his cattle and preserving the environment. The review of applications of smart farming in Ontario such as digital tracking for a cow, genomic testing, digitally signaled birth, sensor driven crop management and data driven dairy production also details in this article.
IRJET - Farm Field Monitoring Using IoT-A Survey PaperIRJET Journal
This document discusses using IoT technology to monitor farm fields. It reviews several existing research papers on IoT-based farm monitoring systems that use sensors to measure soil moisture, temperature, humidity and other factors. However, many of these systems have limitations such as high costs, lack of scalability, and sensors that cannot withstand harsh agricultural field conditions. The document proposes a new IoT farm monitoring system with sensors connected to a microcontroller and cloud platform. This would allow farmers to monitor field conditions remotely using a mobile app or LCD display and receive notifications. The system aims to help farmers increase crop yields while reducing water waste and crop losses.
How Artificial Intelligence, IoT and Big Data Can Save The BeesBernard Marr
Artificial intelligence (AI), internet of things sensors, big data analytics, and other technologies are being used to monitor and develop solutions to mitigate the global issue of declining honey bee and pollinator populations. The World Bee Project and Oracle are using technology to learn more about how to save the bees.
Similaire à A review on internet of things-based stingless bee's honey production with image detection framework (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
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- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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- Access sensitive resources.
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- Create role with administrative privileges.
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- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
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Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
A review on internet of things-based stingless bee's honey production with image detection framework
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 2, April 2024, pp. 2282~2292
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i2.pp2282-2292 2282
Journal homepage: http://ijece.iaescore.com
A review on internet of things-based stingless bee's honey
production with image detection framework
Suziyani Rohafauzi1
, Murizah Kassim2,3
, Hajar Ja’afar1
, Ilham Rustam1
, Mohamad Taib Miskon1
1
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Terengganu, Malaysia
2
School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
3
Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Article Info ABSTRACT
Article history:
Received Jun 9, 2023
Revised Aug 3, 2023
Accepted Dec 5, 2023
Honey is produced exclusively by honeybees and stingless bees which both
are well adapted to tropical and subtropical regions such as Malaysia.
Stingless bees are known for producing small amounts of honey and are
known for having a unique flavor profile. Problem identified that many
stingless bees collapsed due to weather, temperature and environment. It is
critical to understand the relationship between the production of stingless
bee honey and environmental conditions to improve honey production. Thus,
this paper presents a review on stingless bee's honey production and
prediction modeling. About 54 previous research has been analyzed and
compared in identifying the research gaps. A framework on modeling the
prediction of stingless bee honey is derived. The result presents the
comparison and analysis on the internet of things (IoT) monitoring systems,
honey production estimation, convolution neural networks (CNNs), and
automatic identification methods on bee species. It is identified based on
image detection method the top best three efficiency presents CNN is at
98.67%, densely connected convolutional networks with YOLO v3 is
97.7%, and DenseNet201 convolutional networks 99.81%. This study is
significant to assist the researcher in developing a model for predicting
stingless honey produced by bee's output, which is important for a stable
economy and food security.
Keywords:
Convolution neural network
Honey production
Image detection
Internet of things
Stingless bee
This is an open access article under the CC BY-SA license.
Corresponding Author:
Murizah Kassim
Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA
40450 Shah Alam, Selangor, Malaysia
Email: murizah@uitm.edu.my
1. INTRODUCTION
Modern agriculture is recognized as one of the world's most important economic pillars, providing
for an essential human need despite several challenges, such as the rising demand for agricultural products
[1]. One agricultural subsector where new technology might boost productivity, reduce production costs, and
simplify the operations of rearing and breeding bee colonies is beekeeping [2]. To achieve its objective of
developing into a high-income nation, Malaysia requires new high-income initiatives. One such potential
project is the ruthless beekeeping industry's contribution to agriculture and society. Food, medicine, diet
cosmetics, and other related applications are the main industries that employ the stingless bee product.
Although Malaysia has discovered over 38 species of stingless bees, only four are widely used for farming:
Heterotrigona, Geniotrigona thoracica, Heterotrigona itama, Lepidotrigona terminata, and Tetragonula
leviceps [3]. On the other hand, are two species that are particularly active in the production of honey and
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propolis [4]. Stingless bees are pollinators who collect nectar and pollen from a wide variety of plants.
Productive yields of stingless bee honey are predicted to alter the honey industry's supply and demand [5].
Despite this, beekeepers always confront a few difficulties when producing stingless bees in their
colonies. Certain bee species, such as stingless bees, are highly vulnerable to climatic changes, particularly
intense heat waves [6]. Beehive security, health, and weather conditions are a few instances of factors that
could have an impact on the extraction of honey [7]. Bee mortality, particularly that of pupae, has been
scientifically related to temperatures that reach as high as 38 °C [8]. A third of the globe's food supply relies
on honeybees (Apis mellifera) for pollination. The production of honey by stingless bees is considerably less
than that of Apis mellifera. This is why stingless bee honey fails to meet quality control criteria [9].
Temperature, humidity, smoke, and acoustic sounds produced by bee colonies are all important factors that
influence bee productivity. The amount of honey harvested from the hives can be influenced by several
crucial factors. The most common effects of extracting less honey are colony loss and an underproductive
hive. This has an impact on the beekeeper's capacity to maintain hive health and honey production [10]. Both
concerns are exacerbated by the low quality of the stingless bee habitat surrounding the farm. If the bees are
positioned near their food source, a healthy stingless bee colony can be obtained. A lack of food may lead the
bee colony to starve [11]. Certain steps must be taken to prevent the conditions that led to this predicament.
As a result, it is critical to establish a model for predicting the stingless bee's actions so that any stingless
bee's activities may be monitored and sustained.
In recent years, experts from across the world are increasingly interested in the value of modelling
in predicting honey productivity. Several investigators have experimented with the automatic identification
and counting of bees flying into and out of the beehive in conjunction with their investigations of sensory
data. The researchers examined video that was captured with a camera which requires a bee-detecting device
to analyze video streams [12]. Modern beekeepers’ technologies facilitate the creation and use of internet of
things (IoT) monitoring systems built into hives. This makes it feasible to acquire various information and to
monitor remotely in real time. The next agricultural revolution, especially in precision agriculture, will be
largely dependent on machine learning (ML) and the IoT [13]–[15]. Human labor can be minimized by using
machine learning models using sensor data and clever approaches [16]. Insect classification and identification
are two common duties that make use of machine learning techniques, which have recently been discovered
in computer-aided photo classification systems. These challenges have been addressed by deep learning (DL)
models, including convolutional neural networks (CNN), using the picture dataset. In a range of computer
vision (CV) [17] and machine learning [18] problems, CNN has shown exceptional performance. Promising
uses of DL and CV have been shown in the autonomous identification of bees and their health, including the
detection of Varroa destructor and the sense of environmental variables like temperature and humidity [19].
DL is also a sophisticated data analysis and image processing technology that produces decent results and has
a lot of potential. With these improvements, it is possible to classify a bee's health status using photos of the
specimen, CV, and DL. However, to obtain photographs of bees, a previous task that can recognize images of
bees in a scene and automatically crop the discovered bees into a new image must also exist.
A major issue in analyzing social insect behaviors such as bees and flies is the collection of
quantitative data on many social insect behaviors like bees and flies over a long period of time. formerly,
certain investigators relied on beekeepers' manual data collection from beehives [20]. This approach has
worked quite well, but it has become less effective as the hives on a farm has increased. There have been
reports of difficulties in gathering quantitative data on the daily activities of such small and fast-moving
insects [21]. Since stingless bees are non-venomous and smaller in size than ordinary bees, an occurrence
known as colony collapse disorder (CCD) has resulted in a substantial decline of stingless bee colonies
species in 2006. Forager bees are unable to return to the nest due to CCD, leaving the queen and nursing bees
with honey and bee bread [22]. Nonetheless, beekeepers may face some challenges that could lead to colony
failure and underproduction. These issues can be attributed to several factors, including ambient temperature,
humidity, and predators. The application of machine learning techniques in smart monitoring systems is still
in its early stages, but it is rapidly gaining favor. As a result, the contributions of deep learning techniques to
addressing the difficulties of smart monitoring systems in data processing and decision-making have been
highlighted in this study. In comparison to Apis mellifera honey, the previous researcher discovered a
comparatively low yield of stingless bee honey. According to [23], the amount of pure honey produced has
gradually declined in recent years, owing mostly to inappropriate honey brood treatment.
Figure 1 illustrates that this research is significant because it aims to construct a stingless bee IoT
monitoring system at Pusat Penyelidikan Kelulut, UiTM Terengganu Branch. Thus, it served as a site project
and offered information about stingless bee behavior. Sensor data will aid academics and beekeepers in their
work while also catalyzing bee conservation initiatives. Furthermore, through premium honey, this research
will turn the stingless bee honey sector into a sustainable source of income for the Kelulut Research Centre
and Malaysian beekeepers. Finally, the purpose of this study is to create a model for predicting stingless bee
honey output, which is important for a stable economy and food security.
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Figure 1. Stingless bee research centre, Universiti Teknologi MARA Terengganu Branch
Proper management of stingless bee colonies is crucial for their health and productivity. Beekeepers
must ensure that the hives provide suitable conditions for the bees' growth and development. Traditionally,
this involved manual inspections and observations, which were time-consuming and often disruptive to the
bees. The development of innovative monitoring systems, such as IoT technology, has significantly improved
colony management. These systems allow real-time tracking of hive conditions, including temperature,
humidity, and food availability. Beekeepers can now make informed decisions based on accurate data
without disturbing the bees, leading to more effective and efficient hive management. The impact of
environmental factors on stingless bee behavior and honey production is a critical area of concern. Thus, this
research is taken to analyze data taken form real honeybee hive and develop the predictive model on the
honeybee production using CNN based on image processing. Lastly this research aims to validate the
effectiveness of the developed model of stingless bee honey production. Result from this paper can guidance
on the proactive approach allows beekeepers to take timely action and implement appropriate treatments to
safeguard their colonies.
2. METHOD
The most effective approach to discover the review structure is to use an established publishing
database's online search tool. The review structure for the honey production process of IoT-based stingless
bees with image detection is shown in Figure 2. Many of the publications and proceedings that were picked
for the literature review were found by those reviewers searching through the IEEE Xplore database, which is
indexed in Science Direct and Scopus. Due to its availability of all kinds of publications encompassing peer
reviews of products from databases, Google Scholar has also been utilized. The selection of search keywords,
including CNN, stingless bee, IoT system, honey production, and image detection, was guided by the
research framework. Every review paper was selected over a five-year period, starting in 2018 and
concluding in 2022. Most of the journals and proceedings were chosen from 2018 to 2020. Only a few
journals and proceedings were chosen between the years 2016 and 2017. The total number of review papers
is about 54 papers.
Review
Research:
IEEE Database
Scopus
Science Direct
Analyze
Comparison
Research
Gap
Review
Result
Journal;
Proceeding
Stingless Bee
IoT System
Honey Production
Image Detection
CNN
Figure 2. The review of IoT-based stingless bees' honey production with image detection framework
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2.1. Design of IoT stingless bee
Extended transmission range is one of the benefits of low power wide area networks (LPWAN).
Comparing IoT communication technology to current wireless communication technologies, the former offers
greater coverage capacity, lower costs, and lower power usage [24]. The purpose of this study is to address the
long-distance communication needs of intelligent agriculture systems by establishing an information
transmission system using a long-range (LoRa) wireless network and connecting it to an upper computer
system and sensor control system. Figure 3 shows how the IoT monitoring system for the stingless bee is
designed. The components of this setup are gateways with a star topology configured and LoRa wide area
network (WAN) end nodes. End nodes deliver payloads, such as sensory data, to the gateway to communicate.
The gateway is constantly scanned for incoming data packets and that requires an internet connection to
forward them to the things network (TTN) server. Using a node-red application, the data stream will be
collected from the TTN server and pushed to Hostinger, a web hosting service. The data stream was saved in a
MySQL database, which was subsequently connected to a web-based application and servers.
End Node
LoRa
Gateway
IoT Server
Web
Application
The Things
Network
Log 1 Log 2 Log 3 Log 4
LoRa Gateway
TCP/IP
HTTP
LoRa
LoRa LoRa
LoRa LoRa
Node-RED
Figure 3. The architecture of the IoT monitoring system
The current research utilizes a LoRa wireless network to establish an information transmission
system and incorporates it into a sensor control system and an upper computer system. Each end node's data
was transmitted using radio frequency modulation (RFM) via a LoRa shield equipped with an RFM95W
LoRa module. Low-data-rate transmission with strong interference rejection is provided, together with low-
power and long-range spread-spectrum communication capabilities. Data from every sensor connected to the
stingless beehive was handled by the Arduino microcontroller, which operated as the end node's central
processing unit in the interim. A load cell that could measure up to 10 kg was used to track the beehive's
weight over time. The weight data was used to calculate how much honey the beehive produced over time.
Additionally, a smoke sensor that complies with MQ135 was put on each end node to monitor the ambient air
quality. Each beehive's internal temperature and humidity were monitored in the interim using the DHT22
sensor. These components' connections to the beehives are depicted in Figure 4. The cayenne low power
payload (LPP) format was used to combine the data from the temperature, weight, smoke, and humidity
sensors into a single packet stream. These packet streams were routed to the LoRa Gateway once every
60 seconds.
An event-driven application that connects hardware, application programming interface (APIs), and
online services can be made with Node-RED, a freely available open-source programming tool that has a
flow-based editor. In addition to pre-built libraries and functions, it provides components for complex
applications. In this study, data is retrieved from the TTN server and transmitted to the local and hosted
databases using the node-red flow. Through the local network, a node-red user interface (UI) application had
been linked to the local database. Concurrently, an additional database was hosted on the hostinger.com
platform and connected to an online monitoring dashboard application that was viewable from any device
with an internet browser.
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Figure 4. The solar panel, Arduino board, and sensor placement at the stingless beehives
2.2. Stingless bee honey predictions framework
A detailed methodology for precisely forecasting stingless bees' honey production is shown in
Figure 5. This framework is logically divided into four stages, each of which is essential to the process. Phase
1 comprises the preliminary stages of data collection and processing, wherein unprocessed data related to
different variables impacting honey production are obtained and prepared to guarantee uniformity and
stability. During the second phase, known as data selection, the preprocessed data are subjected to in-depth
analysis. This entails using data analysis methods to find relevant traits and trends in a dataset. Finding the
factors that most significantly affect stingless bees' ability to produce honey is the goal. The careful selection
of pertinent data points at this phase enhances the accuracy and effectiveness of later modelling stages. Phase
3, referred to as data development, involves further refining and transforming the selected dataset. Building
an organized dataset is required for both training and verifying predictive models. Normalization, feature
engineering, and data augmentation are a few of the preprocessing techniques that are used to guarantee the
best possible model performance. The prediction model is trained using the improved dataset. Phase 4, which
includes the model validation process, is the framework's climax. In this case, a CNN prediction model is
used. CNNs are the best choice for this forecasting attempt because of their ability to recognize intricate
spatial relationships within data. CNN takes advantage of the meticulously selected and cleaned dataset from
phase.
Camera
IoT System
Data Acquisition and Processing
Gas Sensor
Humidity
Sensor
Temperature
Sensor
Data
Collection
Weight
Sensor
Input
Model
Development
Training
Sample
Testing
Sample
Model
Validation
Final CNN
Prediction
Model of
Honey
Production
Validation
CNN Model
Output
Stop
Publish
Parameter
Data and
Testing Data
Descriptive
Analysis
Min Max Mean
Correlation and
Regression
CNN
Image Acquisition
and Processing
Start
Data Selection
Figure 5. Framework of modelling the prediction of stingless bee’s honey
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3. RESEACH GAPS
Initially, local beekeeping concentrated on several Apis species, such as apis cerana is a type of a
local bee, apis mellifera is a commercial and imported bees and apis dorsata is the wild native giant bee 23.
After the Malaysian Agricultural Research and Development Institute (MARDI) and the YAS began stingless
beekeeping in the Malaysia Agro Exposition Park Serdang (MAEPS), Selangor, in 2011, the total number of
beekeepers surged substantially, reaching around 1,000 persons. The physical traits of their wings distinguish
stingless bees from other bees such as weak or absent sub-marginal cross veins and a second recurrent vein in
the forewing. All stingless bees live in colonies of tens to hundreds of thousands of workers and, in most
cases, a single queen. The number of men in the colony at any given time can range from zero to a dozen;
males and workers are usually comparable in size and appearance, whereas queens are morphologically
unique [25]. In general, stingless bee honey has a lower yield than honeybee honey. The market price of
stingless bee honey, on the other hand, is higher than that of honey bee honey [26]. However, stingless bees,
like many insect groups around the world, are endangered. The stingless bee business in Malaysia is facing
serious issues, especially regarding queens and quality inconsistencies [27], which are threatened by habitat
degradation, global climate change, and resource rivalry. Thus, beekeepers and researchers need to create a
new and proper system to monitor and manage the environmental parameters of beehives as well as boost
honey output.
3.1. IoT monitoring system
IoT is broadly used in many fields of smart agriculture [28]. There are many existing
implementations of computerized identification on irrigation systems [29], pest detection [30], butterflies
[31], crop production [32], varroa mites [33], and so on. However, the identification of the stingless bee relies
very much on manual inspection by visually inspecting the hives and investigating their surroundings for
possible problems. Because of the bees' habits, beekeeping is usually done away from homesteads [34]. As a
result, farmers could lose a lot of money if they do not keep an eye on it regularly. So, the significance of this
research provides automatic inspection as an option to migrate from conventional monitoring into an IoT
monitoring system which provides more efficient monitoring of honey production as well as can increase
yield production. The development of a stingless beehive monitoring system named iBees for Nature and
Trigona Garden, which is in Kemaman, Terengganu has been presented. The system was developed to
monitor the beehive with global positioning system (GPS) tracking, temperature and humidity sensor, and a
microcontroller that combines with Wi-Fi. All the data such as longitude, latitude, temperature, and humidity
are sent to an online cloud database. One previous research has shown a beekeeper can monitor their beehive
in real-time as well as the system can trigger a notification to a user when any abnormal incidents happen to
their hive and keep track of stingless beehive stolen. Meanwhile, the author of designed internet of things
sensor systems embedded in beehives to collect heterogeneous data and provide real-time remote
monitoring [35].
This study was carried out to forecast the processes that take place in beehives based on the
outcomes of intricate prediction models that are used following the manipulation and normalization of the
data. Two-class boosted decision trees, two-class averaged perceptron, two-class bayes point machines, and
two-class support vector machines (SVM) are the algorithms that were covered in this paper. The two-class
boosted tree approach, which has a 99% success rate in data classification and a 30% training set,
demonstrated the best metrics among these algorithms after testing the data. New patterns between the hive's
operations and environmental factors like temperature, humidity, air pressure, CO2, and others can be found
using datasets from beehives. Beekeepers have observed considerable declines in their hives in recent years
because of a phenomenon known as CCD. Over the past few decades, there have been various attempts to
identify the disaster's origins and potential solutions. A real-time automated beekeeping system was
introduced by one system, which offers various insights regarding the beehives' weight, temperature,
humidity, and pressure because of disruptions [36]. A DHT11 temperature and humidity sensor, an Arduino
Uno based on the ATmega328P, a HX711 load cell sensor, and an FSR402 force sensor coupled to an
ESP8266 Wi-Fi module for internet access are the components of a wireless mobile application that may be
used to monitor these data. The interface created using the Blynk platform shows all available data and an
alert sound for emergencies that require attention like sudden pressure surges exerted on the beehive log and
temperatures beyond 40 °C.
To better understand bee behavior and assess the hives' overall health, numerous researchers have
recently attempted to analyze audio and video recordings recorded at the beehives. a mechanism for a device
that kept an eye on a beehive and informed the caretaker on the condition and efficiency of the colony.
Temperature, humidity, and noise levels are measured by the system from within the hive. To track activity at
the beehive entrance and monitor bee activity, a camera is put on the outside of the hive [37]. Algorithms
analyze all this data to notify the beekeeper about the hive's health. The majority of agriculture algorithms
employ machine learning to assess the hive's health included most agriculture prediction on fruits and
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vegetables [38]. A new system's conception and execution for gathering and tracking data was presented. The
technology, called Beemon, is designed to automatically record sensor data, including weight, humidity, and
temperature. Next, a ThingsBoard dashboard receives the sensor data using the message queuing telemetry
transport (MQTT) protocol. Additionally, the Beemon system transfers audio and video recordings that are
collected at the hives' entrance to an external server for examination and additional study. Beemon enables
near real-time data collecting and runs constantly in an outdoor beehive environment.
3.2. Review in honey production
Predictions for stingless bee honey production are used as material to consider and help determine
stingless bee honeybee output. Prediction of honey output is necessary not just for beginners but also for
professional beekeepers or researchers, particularly during months when bees must be relocated owing to
declining food supplies. The use of IoT technology and machine learning in honey production has allowed
beekeepers and other researchers to integrate wireless sensors within beehives, allowing them to regulate the
optimal conditions that promote honey production [37]. Table 1 shows the summary of advantages and
limitations on honey production.
Table 1. Gaps of advantages and limitation on honey production
Area and research filed Method/Algorithm Advantage Limitation
Honey yield prediction using
Tsukamoto fuzzy inference
system [39].
Tsukamoto's fuzzy inference
system (FIS) method
Enable accurate prediction. The FIS limited to the
specific condition and
variables.
Short-term prediction of honey
production in Bosnia and
Herzegovina using IoT [40].
Random tree regression
algorithm
Honey weight production within
the season to adjust the position
of hives.
Data type of the most
challenging seasons for
beekeepers in the Country.
Summer weather conditions
influence winter survival
of honeybees (Apis mellifera)
in the northeastern United
States [41]
Random forest Analysis of climatic variables,
and adverse effects of both too-
cool and too-hot summers were.
The data set.
Machine learning regression
model for predicting honey
harvests [10]
Gradient boosted regression Predict the honey yield per hive
with a mean average error (MAE)
of 10.85 kg and classify the
harvest into the correct quality
category with 92% accuracy.
The accuracy of the
prediction models maybe
restricted by the availability
of weather data.
Analysis of honey production
with environmental variables
[42]
SVM, Multi–layer perceptron
regressor, Random forest
regressor, K-neighbors regressor,
Voting regressor, Ada boost
regressor, Gradient boosting
regressor.
The ensemble learning process
models and increased the
prediction success with the
regression processes.
Algorithm proposed for this
study could not show very
high performance because
they are stand alone.
3.3. Convolution neural network
Numerous computer vision and machine learning tasks have seen CNN perform exceptionally well.
CNN is also used to address a few issues related to agriculture and food production, including remote sensing
[43], plant classification [44], insect detection [45], yield prediction [46], and so forth. The researcher created
a new system that uses video on the colony's entry ramp to identify, locate, and track the body parts of
honeybees [47]. The new technique evaluates CNN's suitability for honeybee pose identification and builds
on recent developments in the field of human pose estimation. Detecting entire bees on the entry ramp with
over 95% accuracy without any prior tracking is possible with the suggested technique. This creates new
opportunities for automated tracking of a wide variety of honeybee behavior outside of the lab and into the
field when paired with trait identification, such as pollen-bearing. A way for creating a beehive health
monitoring system is examined using image processing techniques. Two models make up the method: one
recognizes bees in photos, and the other categories the health of the bees it finds. The defined methodology's
primary contribution is higher model efficiency while keeping the state-of-the-art efficiency constant. Two
databases were used to develop models for convolutional neural networks. The greatest results include 82%
accuracy in identifying bee existence in an image and 95% accuracy in assessing a bee's health. The
possibility of using image recognition methods on honeybee wings to distinguish between different
subspecies of the species was explored using four models based on convolutional neural networks. A total of
9887 wing pictures from 7 subspecies and 1 hybrid were analyzed using ResNet50, MobileNet V2, Inception
Net V3, and Inception ResNet V2 [48]. All models performed better than traditional morphometric
evaluation, with individual wing categorization accuracy rates exceeding 0.92. The Inception models had the
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highest accuracy, recall, and precision scores for many of the classes. In the meanwhile, training has been
conducted using the inception-ResNet-v2 model [46]. The purpose of this work is to identify agricultural
diseases automatically. The data collection, which consists of 27 disease photos from 10 crops, comes from
the 2018 AI Challenger Competition public data set. A multi-layer convolution and a cross-layer direct edge
are features of the residual network unit. Once the combined convolution operation is finished, the link into
the rectified linear unit (ReLU) function activates it. The efficiency of this model is demonstrated by the
experimental findings, which reveal that its total recognition accuracy is 86.1%.
Ten different types of pests that are present in the paddy crop are recognized using deep
convolutional neural networks (DCNN). Around 3,549 pest photos that harm rice crops are included in the
data repository; as deep learning works with larger datasets; data augmentation is done. The ResNet-50
model's layers and hyperparameters are adjusted using the transfer learning technique on the pest dataset. The
model's effective performance in classifying pest diseases is described by the derived resultant value. The
refined ResNet-50 model outperformed the other models with an accuracy of 95.012% when the outcome of
the analysis was compared. CNN ensembles based on several topologies, including ResNet50,
EfficientNetB0, GoogleNet, ShuffleNet, MobileNetv2, and DenseNet201, were created [49]. Various Adam
variations are optimized for pest identification in these topologies. Based on DGrad, two new Adam
algorithms for deep network optimization are put forth that scale the learning rate. Six CNN architectures,
with different optimization functions, were trained using three pest data sets: Xie2 (D0), big IP102, and Deng
(SMALL). Several performance metrics were used to compare and assess the ensembles. The CNNs were
integrated utilizing the various Adam variations by the ensemble that performed the best. All three bug data
sets saw the desired outcome: 95.52% on Deng, 74.11% on IP102, and 99.81% on Xie2. The researcher
created a new system that uses video on the colony's entry ramp to identify, locate, and track the body parts
of honeybees [50]. The updated system expands upon recent advances in CNN for human pose estimation
and assesses its suitability for honeybee pose detection. The proposed system achieves over 95% accuracy in
the pure detection of complete bees on the entrance ramp without any prior tracking. When combined with
trait detection, such as pollen-bearing, this opens new avenues for automated tracking of a diverse range of
honeybee behavior outside of the laboratory and into the field.
3.4. Automatic image detection
There are still few studies on stingless beehives, according to earlier study, although some work has
been done on developing beehive monitoring systems and utilizing CNN to estimate honey production. Thus,
this research proposed the implementation of the prediction of stingless bee honey yield based on bee
movement using CNN. Table 2 shows the articles which discuss the parameters and method used to
determine the potential model of prediction.
Table 2. Automatic detection method
Authors Samples Sensor Method/Technique Efficiency
Xia et al., 2018 [51] 24 species of
crop insects
Image database CNN, Region proposal network,
VGG19
89.22%
Rodríguez et al.,
2018 [47]
Honeybee Digital camera CNN, Hungarian algorithm 95%
Patel and Vaghela,
2019 [52]
Crop Pest Image database CNN 98.67%
Wang et al., 2020
[53]
Pest Image database Deep CNN, VggA, VGG16,
Inception V3, ResNet5, CPAFNet
>92%
Zhong et al.,
2018 [54]
Flying insect Raspberry Pi YOLO, SVM 92.5%
Ai et al., 2020 [48] Insect Pest Dataset from the crop disease
recognition competition of the
2018 Artificial Intelligence
Challenger Competition
Inception-ResNet-v2 86.1%
Nanni et al., 2022
[49]
Pest Deng
(SMALL), large IP102, and
Xie2 (D0) pest data sets
(EfficientNetB0, ResNet50,
GoogleNet, ShuffleNet,
MobileNetv2, and DenseNet201)
95.52% on
Deng, 74.11%
on IP102, and
99.81% on Xie2
Jomtarak et al., 2021
[55]
Mosquito Image database DCNN, YOLO v3 97.7%
Liu et al., 2022 [56] Crop Pest Image database VGG16 and Inception-ResNet-v2 97.71%
Kelley et al., 2021
[57]
Bee Image dataset
Bee spotter
Faster R-CNN, Resnet 101+FPN 91%
Tetila et al., 2020
[58]
Soybean pest Image dataset Inception-v3, Resnet-50, VGG-
16, VGG-19 and Xception, SVM
93.82%
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4. CONCLUSION
As a conclusion a detailed review on IoT-based stingless bee's honey production with image
detection framework has been explored. Results present comparative research gaps in an innovative approach
that uses IoT technology and algorithms to identify and categorize the different bee species. Gaps on honey
production and machine learning techniques related to CNN, YOLO, regression method has been derived.
Lastly, automatic image detection has also been reviewed and presents the best techniques to identify and
predict the honeybee’s production. This result will be used as guidance in the development and deployment
of prediction model for honeybee productivity by using pattern of image processing.
ACKNOWLEDGMENT
Authors acknowledge the Universiti Teknologi MARA, Dungun Terengganu, Malaysia for the
support grants no 600-RMC/YTR/5/3(009/2020).
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BIOGRAPHIES OF AUTHORS
Suziyani Rohafauzi received her bachelor’s in electrical engineering and MSc in
Telecommunication and Information Engineering from Universiti Teknologi MARA (UiTM),
Malaysia. She is a lecturer at the Faculty of Electrical Engineering, Universiti Teknologi
MARA, Terengganu branch. She is currently pursuing her PhD at Universiti Teknologi MARA
(UiTM), Malaysia, working on IoT and machine learning research area. She also a member in
Graduate Engineer, Board of Engineer Malaysia (BEM) (No: G1205352A). She can be
contacted via email: suziyanirohafauzi@gmail.com.
Murizah Kassim is currently working at Institute for Big Data Analytics and
Artificial Intelligence (IBDAAI), Centre of Excellence, Universiti Teknologi MARA, UiTM
Shah Alam. She is an associate professor from the School of Electrical Engineering, College of
Engineering, UiTM Shah Alam, Selangor. She received her PhD in electronic, electrical and
system engineering in 2016 from the Faculty of Built Environment and Engineering, Universiti
Kebangsaan Malaysia (UKM). She has published about 118 indexed papers related to the
computer network, IoT, and web and mobile development applications research. She has
experienced for 19 years in the technical team at the Centre for Integrated Information System,
UiTM. She is also an associate member of Enabling Internet of Things Technologies (Eliott)
research group UiTM. She joined the academic in January 2009 and is currently a member of
MBOT, IEEE, IET, IAENG and IACSIT organizations. She can be contacted via email:
murizah@uitm.edu.my.
Hajar Ja’afar is senior lecturer of radiofrequency and microwave, IoT system at
Univerisiti Teknologi MARA, Terengganu and fellow researcher at Antenna Research Center
based in Univerisiti Teknologi MARA, Shah Alam since 2016. She was awarded in PhD in
Electrical Engineering from Universiti Teknologi MARA in February 2016. She has published
more papers including high impact journal and international conference papers. She also as an
active member in Graduate Engineer, Board of Engineers Malaysia (BEM) (No: 97896A), The
Institution of Engineers, Malaysia (IEM) (No: 36254), Member (M) of IEEE (No: 92628020),
IEEE Antennas and Propagation Society, International Association of Engineers (IAENG)
(No: 193643), Malaysia Board of Technologists (MBOT) (No: PT20090126). She can be
contacted via email: hajarj422@uitm.edu.my.
Ilham Rustam is a senior lecturer Universiti Teknologi MARA Cawangan
Terengganu Kampus Dungun. His research work is currently focused on machine learning
with specific application on wheel mobile robot and stingless bee. He received his PhD in
Electrical Engineering in 2016 from Universiti Teknologi MARA. He has published several
indexed papers related to machine learning, robotics and system identification research. He is
also a certified Professional Engineer (P122173) from Board of Engineers Malaysia (BEM)
and Certified Energy Manager (CEM-MY-1318-0318) from ASEAN Energy Management
Scheme. He can be contacted via email: ilhamr480@uitm.edu.my.
Mohamad Taib Miskon received his B.Eng. degree in electrical (mechatronics)
from Universiti Teknologi Malaysia (UTM), Malaysia and M.Sc. in telecommunication and
information technology from Universiti Teknologi MARA (UiTM) Malaysia. He is currently a
Ph.D student at the Center for System Engineering Studies, Universiti Teknologi MARA
(UiTM), Malaysia. (IoT) application, embedded system design and microcontroller system. He
has published over 20 papers in journals and international conferences. He also registered as a
member of Institute of Engineer Malaysia (IEM) since 2015 and IEEE member since 2021. He
works as a lecturer at Universiti Teknologi MARA, Terengganu, Malaysia since 2009.
Currently his research and teaching interests include process control, internet of things (IoT)
application, embedded system design and microcontroller system. He also registered as a
member of Institute of Engineer Malayisa (IEM) since 2015 and IEEE member since 2021. He
can be contacted via email: moham424@uitm.edu.my.