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.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
This document summarizes research on classifying rice diseases using self-optimizing machine learning models and edge computing. The researchers used an automated machine learning platform to build models for identifying 3 classes of rice diseases from images. They extracted features from the images using a deep learning network and then used those features to train traditional machine learning models like ExtraTrees Classifier and Stochastic Gradient Descent. The goal was to develop an end-to-end solution for rice farmers to easily and accurately detect diseases in the field and receive treatment recommendations in real-time.
Artificial intelligence and robotics.pptxSumant Saini
The document discusses how artificial intelligence is transforming the pharmaceutical industry. It describes how AI is accelerating drug discovery by analyzing vast datasets to identify promising drug candidates faster. AI is also improving clinical trials by helping select optimal patients and design virtual trials. Additionally, AI optimizes manufacturing through predictive maintenance, quality control, and process optimization. The future of AI in pharma includes personalized medicine, drug repurposing, and continuous innovation.
This document summarizes an event bringing together leaders in agriculture and computational informatics to discuss cross-disciplinary opportunities. The objectives were to discuss experiences and challenges, provide opportunities for early career researchers, and use the outcomes to inform research planning. Key topics included big data, modeling, emerging science priorities in areas like genomics and breeding, and transforming information and decision-making workflows. The challenges discussed included improving productivity, developing competitive products and services, and addressing global agricultural issues through collaboration and managing risk/uncertainty.
Computer based dissemination of agricultural
information, expert Systems and decision support systems
(DSS) play a pivotal role in sustainable agricultural
development. The adoption of these technologies requires
knowledge engineering in agriculture. Diversification in
application, spatio-temporal variation, and uncertainty in
environmental data pose a challenge for knowledge
engineering in agriculture. Wheat production management
decision in Pakistan requires acquisition of spatio temporal
information, capturing inherent uncertainty of climatic data
and processing information for possible solution to problems.
In this paper a frame work for engineering of knowledge base
and soft computing model for production management of
wheat crop is presented The frame work include an ontology
based knowledge representation scheme along with structured
rule based system for query processing. A soft computing
model for acquisition and processing of wheat production
information for decision support is presented along with
knowledge delivery through semantic web.
prospects of artificial intelligence in agVikash Kumar
This document provides an overview of artificial intelligence (AI) and its applications in agriculture. It discusses how AI is used in agriculture for automated farming activities, pest and disease identification, crop quality management, and environmental monitoring. The document also covers perspectives on AI progression, from narrow to general to super AI. It discusses recent AI developments in India and applications in agriculture like precision farming, yield prediction, and optimized resource use. Limitations of AI include data and infrastructure challenges. The document concludes that AI can boost agriculture through optimized resource use and complement farmer decision making.
This document summarizes the applications of seed image analysis in seed science research. It discusses how image analysis can be used for varietal identification, characterization, and germination testing. Seed image analysis involves acquiring digital images of seeds and using computer programs to extract quantitative data on seed characteristics like size, shape, color and texture. This data can then be used to automatically classify and identify seed varieties. The document outlines several studies that achieved 98% or higher accuracy in classifying different wheat and bean varieties using seed image analysis. It also discusses how the technique can be applied to testing seed germination and distinguishing new varieties for plant breeding programs.
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.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
This document summarizes research on classifying rice diseases using self-optimizing machine learning models and edge computing. The researchers used an automated machine learning platform to build models for identifying 3 classes of rice diseases from images. They extracted features from the images using a deep learning network and then used those features to train traditional machine learning models like ExtraTrees Classifier and Stochastic Gradient Descent. The goal was to develop an end-to-end solution for rice farmers to easily and accurately detect diseases in the field and receive treatment recommendations in real-time.
Artificial intelligence and robotics.pptxSumant Saini
The document discusses how artificial intelligence is transforming the pharmaceutical industry. It describes how AI is accelerating drug discovery by analyzing vast datasets to identify promising drug candidates faster. AI is also improving clinical trials by helping select optimal patients and design virtual trials. Additionally, AI optimizes manufacturing through predictive maintenance, quality control, and process optimization. The future of AI in pharma includes personalized medicine, drug repurposing, and continuous innovation.
This document summarizes an event bringing together leaders in agriculture and computational informatics to discuss cross-disciplinary opportunities. The objectives were to discuss experiences and challenges, provide opportunities for early career researchers, and use the outcomes to inform research planning. Key topics included big data, modeling, emerging science priorities in areas like genomics and breeding, and transforming information and decision-making workflows. The challenges discussed included improving productivity, developing competitive products and services, and addressing global agricultural issues through collaboration and managing risk/uncertainty.
Computer based dissemination of agricultural
information, expert Systems and decision support systems
(DSS) play a pivotal role in sustainable agricultural
development. The adoption of these technologies requires
knowledge engineering in agriculture. Diversification in
application, spatio-temporal variation, and uncertainty in
environmental data pose a challenge for knowledge
engineering in agriculture. Wheat production management
decision in Pakistan requires acquisition of spatio temporal
information, capturing inherent uncertainty of climatic data
and processing information for possible solution to problems.
In this paper a frame work for engineering of knowledge base
and soft computing model for production management of
wheat crop is presented The frame work include an ontology
based knowledge representation scheme along with structured
rule based system for query processing. A soft computing
model for acquisition and processing of wheat production
information for decision support is presented along with
knowledge delivery through semantic web.
prospects of artificial intelligence in agVikash Kumar
This document provides an overview of artificial intelligence (AI) and its applications in agriculture. It discusses how AI is used in agriculture for automated farming activities, pest and disease identification, crop quality management, and environmental monitoring. The document also covers perspectives on AI progression, from narrow to general to super AI. It discusses recent AI developments in India and applications in agriculture like precision farming, yield prediction, and optimized resource use. Limitations of AI include data and infrastructure challenges. The document concludes that AI can boost agriculture through optimized resource use and complement farmer decision making.
This document summarizes the applications of seed image analysis in seed science research. It discusses how image analysis can be used for varietal identification, characterization, and germination testing. Seed image analysis involves acquiring digital images of seeds and using computer programs to extract quantitative data on seed characteristics like size, shape, color and texture. This data can then be used to automatically classify and identify seed varieties. The document outlines several studies that achieved 98% or higher accuracy in classifying different wheat and bean varieties using seed image analysis. It also discusses how the technique can be applied to testing seed germination and distinguishing new varieties for plant breeding programs.
use of different artificial intelligence tools like tags, sensors, algorithms, computer vision system etc. for better post harvest management of fruit crops with modification in fruits.
528Seed Technological Development – A Surveyidescitation
This paper provides a review of automating or semi-automating the seed quality
purity test. Computer vision (CV) technology used in variety of industries is a sophisticated
type of inspection technology; however, it is not widely used in agriculture.The application
of CV technologies is very challenging in agriculture. As CV plays an important role in this
domain, research in this area has been motivated. Several theories of automating seed
quality purity test are briefly mentioned. The reviewed approaches are classified according
to features and classifiers. The methods for extracting features of a particular seed, and the
classifiers used for classifying the seeds, are mentioned in the paper. An overview of the
most representative methods for feature extraction and classification of seeds is presented.
The major goal of the paper is to provide a comprehensive reference source for the
researchers involved in automation of seed classification, regardless of particular feature or
classifier.
SURVEY ON COTTON PLANT DISEASE DETECTIONIRJET Journal
The document discusses using convolutional neural networks (CNN) and image processing techniques to detect diseases in cotton plants. It aims to develop an accurate and efficient disease detection model to allow early detection and prevention of disease spread. The model would analyze features extracted from images of cotton leaves to classify diseases. It is intended to help farmers improve crop management and reduce economic losses from diseases. The document reviews several previous studies on using deep learning methods for cotton disease identification and classification.
ICT applications have revolutionized the food technology industry in several key ways:
1. Precision agriculture utilizes sensors and data analytics to optimize crop yields and resource use.
2. Supply chain management uses technologies like RFID tags and GPS to track food from farm to fork.
3. Food processing automation improves efficiency and hygiene through technologies like robots and automated control systems.
Plant Disease Detection Technique Using Image Processing and machine LearningJitendra111809
This document discusses designing an image processing-based software solution for automatic detection and classification of plant leaf diseases. It aims to identify diseases using image processing and allow for early detection of diseases as soon as they appear on leaves. This would help farmers more quickly diagnose problems and improve crop yields. The document reviews literature on existing work using machine learning and deep learning for plant disease detection. It also discusses challenges farmers face and the benefits an automated detection system could provide like accelerated diagnosis. Feature extraction methods explored include color, texture, shape and morphology analysis to identify diseases. The document concludes an automated system is important for speeding up the crop diagnosis process.
Machine learning techniques are being used in agriculture to analyze large amounts of data and make predictions about various agricultural operations without being explicitly programmed. There are two main types of machine learning used - supervised learning is used for crop yield prediction by training models on labeled datasets, while unsupervised learning is used for soil analysis by finding patterns in unlabeled datasets. Machine learning has the potential to revolutionize agriculture by improving efficiency, reducing costs, and increasing yields.
machine learning a a tool for disease detection and diagnosisPrince kumar Gupta
Machine learning can be used as a modern tool for plant disease diagnosis by developing algorithms that can accurately identify diseases from images. The document outlines the importance of plant disease diagnosis, traditional diagnostic methods, and introduces machine learning and deep learning. It describes the basic steps in machine learning algorithms including data collection, processing, and output. Applications discussed include identifying diseases from images with high accuracy, monitoring crop and livestock health, controlling greenhouse climate, and linking machine learning to decision support systems.
The document describes a proposed system for detecting grain adulteration using deep neural networks. The objectives are to generate training data by labeling grain images, extract shape features to identify adulteration, train a model using online GPU resources, and understand the impacts of adulteration. The proposed system uses image processing techniques like brightness equalization and edge detection to preprocess grain images before segmenting and classifying them using a convolutional neural network model. This automated approach aims to overcome limitations of existing manual inspection methods.
An effective identification of crop diseases using faster region based convol...IJECEIAES
The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
PRINCE KUMAR GUPTA 54157 "Machine Learning : Modern Tool in Plant Disease Dia...PRINCE GUPTA
Machine learning can be used as a modern tool for plant disease diagnosis by developing algorithms that can accurately identify diseases from images. The document outlines the importance of early and accurate plant disease diagnosis. It then discusses traditional diagnosis methods and highlights machine learning and deep learning as current promising approaches. Key steps in machine learning algorithms include data collection, preprocessing, feature extraction, and model training and prediction. Applications discussed include disease identification, crop monitoring, greenhouse climate control, and linking models to decision support systems for farmers.
Artificial intelligence (AI), robotics, and computational fluid dynamics (CFD) have various applications in the pharmaceutical industry. AI can be used for disease identification, personalized treatment, drug discovery, clinical trial research, and improving healthcare. It has the potential to reduce drug development costs and time. Robotics is being used for tasks like packing drugs, labeling, filling, and capping vials to increase automation. Both AI and robotics face challenges like high costs but have promising futures in areas like personalized medicine and improving drug development.
Potato leaf disease detection using convolutional neural networksIRJET Journal
This document describes a study that used convolutional neural networks to detect three types of potato leaf diseases from images - late blight, early blight, and healthy leaves. The researchers trained a CNN model on a dataset of 1500 labeled potato leaf images. They performed data augmentation and used techniques like image resizing, normalization, and random transformations to improve the model's accuracy. The trained model achieved high performance in identifying the three disease classes, as shown by metrics like accuracy, precision, recall and F1-score. The researchers concluded the CNN model can accurately detect potato leaf diseases and help farmers implement targeted interventions to improve crop health and yields.
This research paper introduces a novel application for predicting plant diseases in cotton and potato plants using Convolutional Neural Networks (CNNs).
Separate CNN models were trained on labeled datasets of cotton and potato leaves, each associated with their respective diseases. The primary goal is to employ a fusion of two standard CNN systems to detect various diseases in cotton and potato plants.
Given India's heavy reliance on agriculture, this innovation is crucial to address challenges faced by the sector, including technological limitations, limited access to credit and markets, and the impact of climate change.
Cotton and potatoes are significant crops; this research paper are susceptible to various diseases that can impede their growth and result in substantial yield losses.
The conventional disease detection methods involve manual inspection and disease prognosis, which are time consuming and less accurate. The research showcases the effectiveness of the automated plant disease detection system, with two best models achieving impressive accuracies of 97.10% and 96.94% for cotton and potato plants, respectively.
These results offer promising insights for potential applications in crop management, benefiting the agricultural sector and contributing to increased productivity and profitability.
The document describes a proposed method for classifying rice leaf diseases using convolutional neural networks (CNNs). The method uses transfer learning with a DenseNet-201 model to classify images of rice leaves with different diseases. The model was trained on 1509 images and tested on 647 separate images, achieving an accuracy of 92.46%. Transfer learning helped improve performance given the small dataset size. Future work could involve collecting more images to further increase accuracy and applying other deep learning models for comparison.
Artificial Inteligence in Animal Husbandry.pptxMilindNande2
Artificial intelligence has potential applications in animal husbandry to improve productivity and management. Current uses include automated milking machines, feeding systems, health monitoring technologies, and herd management software. However, adoption faces challenges like high costs, lack of technical support, and farmer uncertainty. Overcoming these barriers will require demonstrations, training, cooperative investment models, and coordination between public and private sectors.
Intersection of AI and Biotechnology.pptxAjazHussain42
The document discusses the intersection of artificial intelligence and biotechnology. It begins with definitions of AI as using machines to mimic human intellect, and biotechnology as applying biology and technology to improve life. It then outlines applications of each in areas like healthcare, agriculture, and more. The intersection holds potential to personalize treatment using genetic data, assist early disease detection, design biological systems, optimize crop breeding, and more. Overall, the convergence of AI and biotechnology is an important field that can help address challenges in healthcare, agriculture and conservation.
Digital transformation in plant protection leads to
o Increased efficiency: Reduced manual labour, operational costs, improved resource allocation, and optimised workflows.
o Data driven decision making: Farmers can make more informed choices based on data-driven insights, leading to better pest and disease management strategies.
o Automation and predictive analytics: Automation of tasks like pesticide application has reduced human error and resource waste. Predictive analytics models optimise preventive measures.
o Monitoring: Digital solutions enable real-time monitoring by using cell phones.
o Knowledge sharing and innovation: Rapid sharing of knowledge, best practices, and information among farmers, researchers, and stakeholders is possible.
Also, digital transformation opens up avenues for communication among farmers, scientists, and government bodies, resulting in a multitude of indirect benefits: scientists gain better data access, governments improve their policy-making processes, and farmers attain increased crop productivity.
Genetic prediction using Machine Learning Techniques .pptxHabtamuAyenew4
This document discusses using machine learning techniques for genetic prediction. It begins with an introduction to genes and genetics. It then discusses how biotechnology and information technology are integrating, with artificial intelligence finding applications in genetic prediction. Machine learning and different types of machine learning like supervised, unsupervised, and deep learning are explained. Application areas of genetic prediction using machine learning are discussed, along with current trends and challenges. Future work with deep learning algorithms for genomic tasks is mentioned. The document concludes that artificial intelligence is outperforming other methods in clinical diagnostics and more research combining different data sources is still needed.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Contenu connexe
Similaire à Role of AI in seed science Predictive modelling and Beyond.pptx
use of different artificial intelligence tools like tags, sensors, algorithms, computer vision system etc. for better post harvest management of fruit crops with modification in fruits.
528Seed Technological Development – A Surveyidescitation
This paper provides a review of automating or semi-automating the seed quality
purity test. Computer vision (CV) technology used in variety of industries is a sophisticated
type of inspection technology; however, it is not widely used in agriculture.The application
of CV technologies is very challenging in agriculture. As CV plays an important role in this
domain, research in this area has been motivated. Several theories of automating seed
quality purity test are briefly mentioned. The reviewed approaches are classified according
to features and classifiers. The methods for extracting features of a particular seed, and the
classifiers used for classifying the seeds, are mentioned in the paper. An overview of the
most representative methods for feature extraction and classification of seeds is presented.
The major goal of the paper is to provide a comprehensive reference source for the
researchers involved in automation of seed classification, regardless of particular feature or
classifier.
SURVEY ON COTTON PLANT DISEASE DETECTIONIRJET Journal
The document discusses using convolutional neural networks (CNN) and image processing techniques to detect diseases in cotton plants. It aims to develop an accurate and efficient disease detection model to allow early detection and prevention of disease spread. The model would analyze features extracted from images of cotton leaves to classify diseases. It is intended to help farmers improve crop management and reduce economic losses from diseases. The document reviews several previous studies on using deep learning methods for cotton disease identification and classification.
ICT applications have revolutionized the food technology industry in several key ways:
1. Precision agriculture utilizes sensors and data analytics to optimize crop yields and resource use.
2. Supply chain management uses technologies like RFID tags and GPS to track food from farm to fork.
3. Food processing automation improves efficiency and hygiene through technologies like robots and automated control systems.
Plant Disease Detection Technique Using Image Processing and machine LearningJitendra111809
This document discusses designing an image processing-based software solution for automatic detection and classification of plant leaf diseases. It aims to identify diseases using image processing and allow for early detection of diseases as soon as they appear on leaves. This would help farmers more quickly diagnose problems and improve crop yields. The document reviews literature on existing work using machine learning and deep learning for plant disease detection. It also discusses challenges farmers face and the benefits an automated detection system could provide like accelerated diagnosis. Feature extraction methods explored include color, texture, shape and morphology analysis to identify diseases. The document concludes an automated system is important for speeding up the crop diagnosis process.
Machine learning techniques are being used in agriculture to analyze large amounts of data and make predictions about various agricultural operations without being explicitly programmed. There are two main types of machine learning used - supervised learning is used for crop yield prediction by training models on labeled datasets, while unsupervised learning is used for soil analysis by finding patterns in unlabeled datasets. Machine learning has the potential to revolutionize agriculture by improving efficiency, reducing costs, and increasing yields.
machine learning a a tool for disease detection and diagnosisPrince kumar Gupta
Machine learning can be used as a modern tool for plant disease diagnosis by developing algorithms that can accurately identify diseases from images. The document outlines the importance of plant disease diagnosis, traditional diagnostic methods, and introduces machine learning and deep learning. It describes the basic steps in machine learning algorithms including data collection, processing, and output. Applications discussed include identifying diseases from images with high accuracy, monitoring crop and livestock health, controlling greenhouse climate, and linking machine learning to decision support systems.
The document describes a proposed system for detecting grain adulteration using deep neural networks. The objectives are to generate training data by labeling grain images, extract shape features to identify adulteration, train a model using online GPU resources, and understand the impacts of adulteration. The proposed system uses image processing techniques like brightness equalization and edge detection to preprocess grain images before segmenting and classifying them using a convolutional neural network model. This automated approach aims to overcome limitations of existing manual inspection methods.
An effective identification of crop diseases using faster region based convol...IJECEIAES
The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
PRINCE KUMAR GUPTA 54157 "Machine Learning : Modern Tool in Plant Disease Dia...PRINCE GUPTA
Machine learning can be used as a modern tool for plant disease diagnosis by developing algorithms that can accurately identify diseases from images. The document outlines the importance of early and accurate plant disease diagnosis. It then discusses traditional diagnosis methods and highlights machine learning and deep learning as current promising approaches. Key steps in machine learning algorithms include data collection, preprocessing, feature extraction, and model training and prediction. Applications discussed include disease identification, crop monitoring, greenhouse climate control, and linking models to decision support systems for farmers.
Artificial intelligence (AI), robotics, and computational fluid dynamics (CFD) have various applications in the pharmaceutical industry. AI can be used for disease identification, personalized treatment, drug discovery, clinical trial research, and improving healthcare. It has the potential to reduce drug development costs and time. Robotics is being used for tasks like packing drugs, labeling, filling, and capping vials to increase automation. Both AI and robotics face challenges like high costs but have promising futures in areas like personalized medicine and improving drug development.
Potato leaf disease detection using convolutional neural networksIRJET Journal
This document describes a study that used convolutional neural networks to detect three types of potato leaf diseases from images - late blight, early blight, and healthy leaves. The researchers trained a CNN model on a dataset of 1500 labeled potato leaf images. They performed data augmentation and used techniques like image resizing, normalization, and random transformations to improve the model's accuracy. The trained model achieved high performance in identifying the three disease classes, as shown by metrics like accuracy, precision, recall and F1-score. The researchers concluded the CNN model can accurately detect potato leaf diseases and help farmers implement targeted interventions to improve crop health and yields.
This research paper introduces a novel application for predicting plant diseases in cotton and potato plants using Convolutional Neural Networks (CNNs).
Separate CNN models were trained on labeled datasets of cotton and potato leaves, each associated with their respective diseases. The primary goal is to employ a fusion of two standard CNN systems to detect various diseases in cotton and potato plants.
Given India's heavy reliance on agriculture, this innovation is crucial to address challenges faced by the sector, including technological limitations, limited access to credit and markets, and the impact of climate change.
Cotton and potatoes are significant crops; this research paper are susceptible to various diseases that can impede their growth and result in substantial yield losses.
The conventional disease detection methods involve manual inspection and disease prognosis, which are time consuming and less accurate. The research showcases the effectiveness of the automated plant disease detection system, with two best models achieving impressive accuracies of 97.10% and 96.94% for cotton and potato plants, respectively.
These results offer promising insights for potential applications in crop management, benefiting the agricultural sector and contributing to increased productivity and profitability.
The document describes a proposed method for classifying rice leaf diseases using convolutional neural networks (CNNs). The method uses transfer learning with a DenseNet-201 model to classify images of rice leaves with different diseases. The model was trained on 1509 images and tested on 647 separate images, achieving an accuracy of 92.46%. Transfer learning helped improve performance given the small dataset size. Future work could involve collecting more images to further increase accuracy and applying other deep learning models for comparison.
Artificial Inteligence in Animal Husbandry.pptxMilindNande2
Artificial intelligence has potential applications in animal husbandry to improve productivity and management. Current uses include automated milking machines, feeding systems, health monitoring technologies, and herd management software. However, adoption faces challenges like high costs, lack of technical support, and farmer uncertainty. Overcoming these barriers will require demonstrations, training, cooperative investment models, and coordination between public and private sectors.
Intersection of AI and Biotechnology.pptxAjazHussain42
The document discusses the intersection of artificial intelligence and biotechnology. It begins with definitions of AI as using machines to mimic human intellect, and biotechnology as applying biology and technology to improve life. It then outlines applications of each in areas like healthcare, agriculture, and more. The intersection holds potential to personalize treatment using genetic data, assist early disease detection, design biological systems, optimize crop breeding, and more. Overall, the convergence of AI and biotechnology is an important field that can help address challenges in healthcare, agriculture and conservation.
Digital transformation in plant protection leads to
o Increased efficiency: Reduced manual labour, operational costs, improved resource allocation, and optimised workflows.
o Data driven decision making: Farmers can make more informed choices based on data-driven insights, leading to better pest and disease management strategies.
o Automation and predictive analytics: Automation of tasks like pesticide application has reduced human error and resource waste. Predictive analytics models optimise preventive measures.
o Monitoring: Digital solutions enable real-time monitoring by using cell phones.
o Knowledge sharing and innovation: Rapid sharing of knowledge, best practices, and information among farmers, researchers, and stakeholders is possible.
Also, digital transformation opens up avenues for communication among farmers, scientists, and government bodies, resulting in a multitude of indirect benefits: scientists gain better data access, governments improve their policy-making processes, and farmers attain increased crop productivity.
Genetic prediction using Machine Learning Techniques .pptxHabtamuAyenew4
This document discusses using machine learning techniques for genetic prediction. It begins with an introduction to genes and genetics. It then discusses how biotechnology and information technology are integrating, with artificial intelligence finding applications in genetic prediction. Machine learning and different types of machine learning like supervised, unsupervised, and deep learning are explained. Application areas of genetic prediction using machine learning are discussed, along with current trends and challenges. Future work with deep learning algorithms for genomic tasks is mentioned. The document concludes that artificial intelligence is outperforming other methods in clinical diagnostics and more research combining different data sources is still needed.
Similaire à Role of AI in seed science Predictive modelling and Beyond.pptx (20)
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
What is greenhouse gasses and how many gasses are there to affect the Earth.
Role of AI in seed science Predictive modelling and Beyond.pptx
1.
2. Presented By –
Nayeem Ul Bashir
31/Ag(SST)/22-M
M.Sc 2nd Year
Divison of Genetics
and Plant breeding
3. What is Artificial Intelligence ?
Artificial Intelligence
(AI) refers to the
simulation of human
intelligence in
machines that are
able to think, learn,
and act intelligently.
These systems utilize advanced computational methods, such as
machine learning
natural language processing
cognitive computing
It enable them to
perform tasks
that typically
require human-
like perception,
decision-making,
and interaction.
4. Overview of Seed Science Predictive Modeling
Predictive modeling in seed science aims to forecast characteristics,
behavior or outcomes related to seeds, often utilizing machine learning
algorithms.
One example of this application involves building predictive models
to determine seed classes, which can enhance crop production and
improve farming practices.
Researchers commonly employ artificial neural networks (ANNs) to
analyze large sets of seed data and create models that accurately predict
seed classes.
6. Historical Perspective
• Seed science has evolved significantly
over time, with traditional methods
giving way to more advanced
techniques, including the integration of
artificial intelligence (AI).
• Historically, seed science focused on
understanding seed dormancy,
germination, and plant biology processes
using conventional statistical methods .
Evolution
of Seed
Science.
7. • Traditional methods in seed science involved analyzing data from
dormancy and germination studies using basic statistical
approaches.
• These methods often fell short in fully understanding the intricate
processes involved in seed biology due to limitations in studying
multiple factors simultaneously.
• They relied on statistical regressions and simple algorithms to
interpret data, which could not capture the complexity of seed
germination and dormancy interactions
Traditional
Methods in
Seed
Science
8. • The integration of AI in seed science has brought
significant advancements in predictive modeling,
understanding seed traits, and optimizing crop
production practices.
• AI tools, such as artificial neural networks (ANN)
shave enabled researchers to predict seed germination,
optimize dormancy processes, and model complex
biological interactions more accurately.
• By leveraging AI technologies, seed scientists can
enhance their ability to analyze large datasets, predict
seed performance, and optimize breeding strategies for
improved crop productivity
Emergence
and
Integration
of AI
9. AI Techniques in Seed Science
A. Machine Learning Algorithms
Supervised Learning for Seed Quality Prediction:
Researchers in Brazil have developed a methodology
based on artificial intelligence to automate and
streamline seed quality analysis using machine learning.
By acquiring images of seeds through light-based
technology and employing chemometrics and machine
learning, they were able to classify seed quality based on
chemical composition with high accuracy
10. Unsupervised Learning for Pattern Recognition:
Artificial intelligence tools, such as artificial neural
networks (ANNs) combined with fuzzy logic, have been
utilized to model and predict seed germination and
dormancy processes. These AI tools offer advantages
over traditional statistical methods by enabling the study
of complex interactions among multiple factors in seed
biology
11. B. Deep Learning Applications
Neural Networks in Germination Prediction:
• Artificial neural networks (ANNs) have been applied to
predict plant biology processes like seed germination.
This technology allows for the modeling and prediction
of seed dormancy release and germination, enhancing
the understanding of these critical processes in seed
science
12. Convolutional Neural Networks (CNN) for Seed
Image Analysis:
Convolutional Neural Networks (CNNs) have
been used for seed image analysis, enabling the
identification of seed varieties, species, and
abnormalities. By leveraging various imaging
techniques and machine learning algorithms,
researchers can distinguish different seed
characteristics effectively.
13. Advancements in Seed Science Predictive
Modeling
Precision agriculture techniques, driven by
AI, aim to optimize crop production by
offering smart farming techniques and AI-
driven crop monitoring. Examples include:
• Smart Farming Techniques: Utilization of AI algorithms
to manage irrigation, fertilizer application, and pest
control, thus improving crop yields and reducing waste
• AI-driven Crop Monitoring: Real-time monitoring of
crop health and growth, facilitating early intervention
and prevention of issues
Precision Agriculture and AI
14. Genomic data and AI are driving
improvements in seed trait
prediction and optimization. Notable
developments include:
Employing genetic
algorithms to accelerate the breeding process
and produce superior crop varieties
Genomic Data and AI
15. Future Direction of Seed
Science Predictive Modeling
A. Integration of AI with Other Technologies
•Integrating AI with existing technologies, such as
remote sensing, drones, and IoT devices, will expand
the capabilities of seed science predictive modeling.
•For example, AI-assisted crop monitoring and
precision agriculture will leverage these technologies
to optimize crop production and minimize waste
16. B. Potential Impact on Global Agriculture
• AI-enhanced seed science will contribute to addressing
global challenges, such as climate change, environmental
concerns, and rising demands for food.
• AI's ability to improve efficiency, sustainability, and
resource allocation makes it a promising tool for meeting
the needs of a rapidly growing global population
17. C. Research and Development Prospects
• Research and development efforts should prioritize creating
accurate and reliable predictive models, integrating AI with
other disciplines, and ensuring that AI-driven solutions are
accessible and affordable for farmers worldwide.
• Additionally, addressing ethical considerations in genetic
modification and data privacy concerns will be crucial for the
successful implementation of AI in seed science.
18.
19. Case Study 1
Objectives: The study aims to develop an automated system for quality testing of corn seeds..
20. Materials & Methods: The researchers created a novel seed image acquisition setup capturing top
and bottom views of seeds, utilized a Conditional Generative Adversarial Network (CGAN) to
generate realistic images for underrepresented classes, and employed Batch Active Learning (BAL)
for efficient data annotation.
21. Result: The proposed system achieved up to 91.6% accuracy in testing the physical purity of seed samples.
22. Conclusion
In exploring the intersection of Artificial Intelligence (AI) and Seed
Science Predictive Modeling, several key findings have emerged:
• AI technologies, such as machine learning and deep learning
algorithms, are revolutionizing seed science by enabling precise
prediction of seed traits, optimizing breeding strategies, and enhancing
crop productivity.
• The integration of AI with other technologies, such as remote sensing
and genetic algorithms, is expanding the scope of predictive modeling
in seed science.
• Challenges such as data quality and availability, ethical considerations in
genetic modification, and the need for interdisciplinary collaboration
must be addressed to fully leverage the potential of AI in seed science.