Real-time agricultural field monitoring and smart irrigation architecture using the internet of things and quadrotor unmanned aerial vehicles

1. Received: 1 January 2022 Accepted: 4 March 2022 Published online: 26 May 2022 DOI: 10.1002/agj2.21061 S P E C I A L S E C T I O N : A DVA N C E S I N R E M O T E S E N S I N G F O R P R E C I S I O N AG R I C U LT U R E Real-time agricultural field monitoring and smart irrigation architecture using the internet of things and quadrotor unmanned aerial vehicles Rajalakshmi Selvaraj1 Venu Madhav Kuthadi1 S. Baskar2 1Dep. of CS & IS, Botswana International Univ. of Science and Technology (BIUST), Palapye, Botswana 2Dep. of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India Correspondence Rajalakshmi Selvaraj, Dep. of CS & IS, Botswana International Univ. of Science and Technology (BIUST), Palapye, Botswana. Email: rajalakshmiselvaraj270@gmail.com Assigned to Associate Editor Priyan Malarvizhi Kumar. Funding information Department of Science and Technology (DST), New Delhi, India, for the funding to carry out the Research work - DST/TDT/AGRO-20/2019 & 22-01-2020 Abstract Farming and agricultural production account for a substantial part of the global economic system, and most people rely on them for their living. In this perspective, real-time agricultural field monitoring and smart irrigation using modern technologies are now important for effective farming in green homes, smart cities, and rural areas. Water is an essential resource to be conserved using the newest technology. The Internet of Things (IoT) and Industry 4.0 enable smart farming, including using Quadrotor unmanned aerial vehicles (Q-UAV) with computer vision. The IoT-based smart irrigation management systems with real-time sensors and Q-UAVs have contributed to the optimum use of water resources in precision farming. The research presented an intelligent irrigation and field surveillance system using atmospheric and soil data such as temperature, humidity, salinity, wind speed, as well as photographs of the field using UAVs. The parameters mentioned above are available on the smartphone of the farmers using IoT and are hosted without any delay in the Firebase console. In addition to this, a user can control the water pump on various fields via Firebase Cloud Message platform. The intelligence and smartness of the proposed system are implemented with a powerful and low-cost platform Raspberry Pi 4B system on chip computer with Industry 4.0 standard dedicated for IoT, real-time embedded protocol interfacing, and computer vision applications. 1 INTRODUCTION Unmanned aerial vehicle (UAV) technology and smart sensors have the potential to assist farmers across the globe in various ways, including monitoring crops, paddy fields, Abbreviations: ADC, analog to digital converter; GPIO, general-purpose input–output; HTTP, hypertext transfer protocol; I2C, inter-integrated circuit; IDLE, Integrated Development Learning Environment; IoT, Internet of Things; Q-UAV, Quadrotor unmanned aerial vehicle; RPi, Raspberry Pi; SCL, serial clock line; SDA, serial data line; SoC, system on chip; SPI, serial peripheral interface; UAV, unmanned aerial vehicle; XMPP, extensible messaging and presence protocol. © 2022 The Authors. Agronomy Journal © 2022 American Society of Agronomy. improving land tenure, and more (A. López et al., 2019). However, to achieve its full potential, regulatory frameworks are required. This paper presented a real-time implementation of a smart agricultural irrigation architecture using the Internet of Things (IoT) and vision-assisted field monitoring via a cloud server, namely the Firebase platform. This architecture offers several significant benefits compared with conventional remote monitoring methods such as satellites and human-crewed aircraft. Using real-time sensor data, the framers can earn more money and reduce production costs by making intelligent decisions based on the current state of the paddy field. Drip irrigation facilities can be made more Agronomy Journal. 2023;115:1–20. wileyonlinelibrary.com/journal/agj2 1 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

2. 2 SELVARAJ ET AL. efficient by sensor data, such as temperature, humidity, and soil moisture. In addition to this, the framework based on the Rasp- berry Pi (RPi) system on chip (SoC) computer can capture high-definition images of crops, animals, and other objects in agricultural fields through direct interaction between farmers and the UAV vehicle via a remote controller or the Firebase cloud server. Perhaps most significantly, this framework with Quadrotor unmanned aerial vehicle (Q-UAV) is cheap, cost- ing less than US$300. Due to the use of customized sensors and open-source programming platforms such as Open Com- puter Vision and Python Integrated Development Learning Environment (IDLE), the overall cost of this system has been lowered to a minimum. The Q-UAV technology is intended to benefit farmers in underdeveloped nations using current tech- nological advancements such as embedded systems and computer vision architecture to assist them in their farming operations. There is no need for a human pilot or travelers to be UAVs for drones, and it is common for drones to be remotely piloted by a human, even though this is not always the case. Agriculture will benefit significantly from UAVs. The UAV data will become increasingly automatic for identifying different crop varieties, categorizing weeds, and assessing crop damage caused by pests, among other things (Albu- querque et al., 2020; Shi et al., 2018). The Q-UAVs with more intelligence may be used for precision agricultural spraying, allowing farmers to use fewer pesticides and reduce human interaction with potentially hazardous compounds while increasing crop yields. Getting sick is a component of living that we have no control over. This means that sickness absence is a part of working life, and it is unavoidable. The strategies to be checked are as follows: 1. A physical or mental ailment that is real. 2. A way of life that is detrimental to one’s health. 3. Caring for loved ones is a necessity. 4. Problems with one’s own emotions. 5. Understanding sick leave policies is a problem. Agriculture continues to account for a significant portion of global commercial growth, and financial investments in the agricultural sector have grown significantly in recent years as a result. Pets and harmful insects reduce the potential production of crops, reducing their overall yield. The use of UAVs for pesticide and fertilizer spraying has significantly decreased the incidence of health problems and the number of employ- ees (Rahman et al., 2021). The UAVs and intelligent monitoring systems with powerful computer platforms are essential components of the agricultural revolution (Kataev et al., 2019; Rodriguez-Galvis et al., 2020). The framers may earn more profit and reduce the production cost using the sensor data gathered in real-time and make intelligent decisions on the current paddy state. Sensor data like temperature, humid- Core Ideas ∙ Internet of Things and Industry 4.0 enable smart farming, which includes the use of Quadrotor unmanned aerial vehicles. ∙ A user can control the water pump on various field via Firebase Cloud Message platform. ∙ The intelligence and smartness of the proposed system is implemented with a powerful and low cost Raspberry Pi 4B. ity, and soil moisture assist farmers in enabling the drip irrigation facility (Mahbub et al., 2020; Ogidan et al., 2019). Inno- vative farming systems based on embedded systems, computer vision, and the IoT are gaining popularity and interest to increase food production (Cao et al., 2019; Serdaroglu et al., 2020). During the automate to detect different crop varieties, classify weeds, and analyze crop damage caused by pests, the cropping calendar encompasses everything from land prepa- ration to planting to harvesting. Crop spectral reflected light is referred to as the crop’s temporal profile at each of these stages of growth. The research work presented using IoT and computer vision has two main parts. Firstly, Q-UAV development provides remote vision-based monitoring with RPi 4B hardware and a Firebase cloud platform. The second component plays an integral part in automation, including environmental and soil factors that directly influence crop production and the sustain- ability of the agricultural community. There is no need for a human pilot or travelers to be UAVs for drones. It is common for drones to be remotely piloted by a human, even though this is not always the case. The proposed framework with digital and analog sensors measures the real-time physiological parameters. The irrigation system and other automatic devices are controlled by the general-purpose input–output (GPIO) lines of the RPi 4B model. The following sections have been included in this research article: some relevant works (section 2), system architectures for smart farming, including hardware and sensor interfacing, protocols and cloud applications (section 3), application of Q-UAV on agriculture field monitoring (section 4) and real-time experimental set-up (section 5) followed by the conclusion. 2 RELATED WORK Tiglao et al. (2020) have suggested a wireless sensor and actu- ator network-based Agrinex system (WSAN). The framers may earn more profit; furthermore, the sensor data gathered in real-time made intelligent decisions on the current 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

3. SELVARAJ ET AL. 3 paddy state. Sensor data like temperature, humidity, soil moisture, etc., could facilitate farmers to enable the drip irrigation facility. Their mesh network has been developed to reorganize the sensors depending on the weather conditions. Canales-Ide et al. (2019) have created a Water Use Land- scape Species Classification (WUCOLS); it estimates the plant water requirements based on plant species composites. Humans manage irrigation scheduling and operation by cal- culating crop coefficients and irrigation frequency based on climatic variables. Zhu et al. (2021) studied optimum routing, aborting, and striking methods for UAVs. They considered parameters such as fuel load, window time, and other variables, and their model reduced the likelihood of UAVs being destroyed. Architectural strength is used in interconnected and intelligent smart agriculture to address privacy and security issues. Their multi-faceted design resolved cyber attacks in the food system, internet security issues, and most substantial difficulties and covers in these smart agriculture issues. Bu and Wang (2019) have developed an IoT and machine learning architecture in an intelligent agricultural environment; enhances food production via the use of today’s technology such as artificial intelligence (AI) and cloud computing. In particular, profound enhancements in the cloud layer lead to rapid choices such as the water must be rinsed to improve the culture-growth environment. Reghukumar and Vijayakumar (2019) suggested IoT-based real-time agrifarm monitoring and decision-making minimize farmers’ effort by busing intelligent sensors and actuators. Their concept over- comes the limitations of state-of-the-art techniques with intelligent farming via IoT and helps farmers assess their portable gadget data. It removes the essential requirement for continuous human surveillance on their paddy fields. Gupta et al. (2020) developed a robust architecture in intelligent and interconnected smart farming to resolve data privacy and safety concerns. Their multi-faceted design addressed cyber assaults in the food supply chain, cybersecurity problems, and the most significant difficulties and research concerns in these intelligent agriculture issues. In precision agriculture, C. López et al. (2021) addresses the issue of picture fusion. They created a multi-layer regression model for UAV pictures, enabling aerial images with substantial differences to be connected. The effectiveness of the Enhanced Correlation Coefficient technique is an adequate way to record diverse views. Allreda et al. (2020) presented a new way of finding drainage pipes using thermal infrared (IR) imaging. Three techniques have been highlighted: visible color, multispectral, and thermal infrared imagery using UAVs. The models mentioned above offer considerable mapping potential for agricultural drainage pipes. Li and Fang (2021) have developed and classified the formation decision function in UAV virtual point formation control mode at an anticipated angle based on the pigeon swarm behavior tracking model. Their simulation findings prevent colli- sions between UAVs and different barriers, effectively control UAVs, and extend the UAV swarm application. An IoT and machine learning architecture in an intelligent agricultural environment improves food production. When the cloud layer improves significantly, rapid decisions are made, such as rinsing the water to improve the culture-growth environment. Liao et al. (2021) have created an intelligent irrigation system using real-time soil parameter monitoring. They stressed the significance of irrigation planning and provided insights into designing an effective and automated irrigation system. Nawandar et al. (2019) highlighted the need for natural resource conservation and justified the need for intelligent, automated systems. Their standardized approach based on IoT and industry 4.0 enhances the demand for intelligent sensors in agriculture, particularly irrigation and pest management. The neural network gives the device the necessary intelligence, considering current sensor information and masking the irrigation schedule for adequate watering. Podder et al. (2021) presented a rural community IoT-based Smart AgroTech system considering crucial characteristics of soil and environment for agricultural fields. The choice of the AgroTech system on irrigation relies on the farming circumstances. The vendor may see and analyze the sensor data through a remote monitoring system. The system ensures that the agricultural operations in future cities benefit from a viable Smart AgroTech system that other tradi- tional techniques. The Improved Multiple Regression technique’s effectiveness is adequate for recording various points of view. Thermal IR imaging provides a new method for locating drainage pipes. It has been discussed how UAVs can capture appearance, hyperspectral, and near-infrared imagery. Based on a literature study analysis, it is evident that there are many intelligent irrigation methods and UAVs to main- tain friendly and cost-effective farming. Current systems have some limits, such as flexibility with farmers, optimum IoT and sensor platforms, multiple node systems, conserving water and electricity, etc. This article highlights the improvement of intelligent irrigation and real-time field monitoring in rural and urban agriculture. The primary goals of the system suggested are as follows: 1. Real-time deployment of IoT and computer vision for automated irrigation control systems. 2. Deploy an intelligent and low-cost irrigation system with a soil moisture sensor, temperature, rain indicator, wind speed, and humidity sensor. 3. Develop an intelligent agriculture-filed monitoring system that collects real-time filed images using Q-UAVs, thus lowering labor costs. 4. Analyzes the importance of bringing the suggested smart system into a rural and innovative agricultural environment and set the user interface to a Firebase cloud. 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

4. 4 SELVARAJ ET AL. 3 METHODS AND TECHNOLOGIES USED IN INTERNET OF THINGS BASED SMART IRRIGATION SYSTEM FOR AGRICULTURAL FILED This section presented an intelligent agricultural field surveillance system using smart sensors and a powerful GPU computing platform. The suggested framework is mainly developed using RPi 4B; it is a SoC platform with Broadcom BCM2711, Quad-core Cortex-A72 (ARM Version 8) 64-bit SoC @ 1.5 GHz 8GB LPDDR4- SDRAM. Linux or Windows operating system power this low-cost platform with a credit card-sized platform. This computing platform includes data processing to improve decision-making and supports it. Architectural strength is used in interconnected and intelligent smart agriculture to address privacy and security issues. Their multi-faceted design resolved cyber attacks in the food system, internet security issues, and most substantial difficulties and covers in these smart agriculture issues. The RPi is a low-power SoC computer with customizable GPIO pinouts, a robust CPU that can run Linux, and supports NodeJS, making it easy to create complex devices. In general, it is the route to Industry 4.0, which supports IoT, 5G connection, and artificial intelligence automated industrial systems. The next part discusses the complete architecture of this suggested system, including the real-time sensor implementation aspects. Various analogue and digital sensors are used to measure atmospheric and soil parameters, which are described in Figure 1. The proposed system is implemented with five later architectures; the bottom layer collects real-time sensor data. In this physical layer, a sensor like DS18B20, DHT11, customized soil moisture, wind speed, and rain sensors are connected to the RPi SoC unit using inter-integrated circuit (I2C) protocol and MCP 3008 analog to digital converter (ADC). An I2C protocol is used to communicate with low- speed peripherals. Depending on your board’s model and revision, there may be one or two I2C buses on it. The serial data line (SDA) and a serial clock line (SCL) are the two input lines on each bus connected to an I2C center. Analog sensors are interfaced to RPi via the ADC eight-channel module; this module has provided the digital conversion process and communicated through the I2C protocol bus to the RPi system. The Firebase platform configures a real-time database; it is a dedicated cloud architecture supporting images, audio, and video streaming applications. Conservation is the preser- vation and safeguards of these resources to preserve these resources for the future. Preserving nature from human inter- ference is the goal of conservation; on the other hand, conservation aims to sustain human activities like predation, able to log, and mining. This research aims to develop a low-cost and accessible smart irrigation system for rural and farmer’s communities by using technical progress in sensors and embedded system sectors. The suggested system thus introduces a Q- UAV framework for real-time monitoring and sensors for the cloud- based irrigation management mechanism. The sensors used are described in the following subsections. The number of power strips should be restricted. An overheating problem can occur if too many power strips use a single outlet. Regularly hire a professional electrician to inspect your wiring for signs of wear and tear. Perform an appliance inspection. The IoT architecture used in this research is based on different sensors, layers, and functionalities, as shown in Figure 2. The IoT is a linked network of physical devices, sensors, and software-embedded protocols. The developed architecture has been integrated into small business and agriculture areas, providing end-to-end solutions by combin- ing IoT and computer vision characteristics. This research transforms IoT systems seamlessly, providing complete device management and real-time monitoring. Users may access sensor data in a standard cloud database without interruption when using a mobile internet connection. By using extensible messaging and presence protocol (XMPP) and hypertext transfer protocol (HTTP), the proposed system’s internet layer can communicate the water pump status to the remote sensor module (HTTP). Figure 2 shows each layer representing the corresponding role for smart irrigation and UAV implementation in the agricultural field. The suggested system is multi-configured; it works in wholly automated mode. Sensor values are shown in the cloud database in the standard architecture and may access the user without interruption through their mobile internet. The internet layer of the proposed system has included a unique capability to communicate the water pump status with a remote sensor module using XMPP and HTTP. This function allows the user to turn the motor ON/OFF through the Firebase interface. This research bids real-time monitoring and control of motor pumps and other agricultural equipment without human presence. It will enhance the efficacy and safety of human beings and prevent overheating and other current surge issues for electrical and mechanical equipment. The following section provides a comprehensive examination of layered architecture. Connected devices, sensors, and software-based protocols form the IoT. End-to-end solutions that combine IoT and computer vision characteristics have been implemented in small businesses as well as in agriculture. The research and analysis have been conducted in three main agriculture fields. The performance of developed hardware was tested on 0.3 ha of the vegetable greenhouse, 1 ha of mixed plants such as tapioca (Manihot esculenta Crantz) and banana (Musa X paradisiaca L.), and 1 ha of coconut (Cocos nucifera L.) plantation. For these three fields, average temperatures vary, and average sunshine hours of 9– 10 h. The research was split into a three-tier structure: from March to June, July to October, and November to February in 1 yr. Agricultural production is based on many variables; key 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

5. SELVARAJ ET AL. 5 F I G U R E 1 The architecture of smart irrigation systems using Internet of Things. ADC, analog to digital converter; GPIO, general-purpose input–output; HTTP, hypertext transfer protocol; SCL, serial clock line; SDA, serial data line; XMPP, extensible messaging and presence protocol characteristics include carbon dioxide, temperature, solar irra- diation, precipitation, soil moisture, wind speed, direction, etc. The detailed analysis of sensors used in this research is available in the subsequent sessions. The proposed system’s internet layer includes a unique feature that allows the remote sensor module to communicate with the water pump’s status via extensible messaging. 3.1 Embedded protocol layer The primary communication need for the interaction of hardware and memory processing units is the internal communication or on-board communication protocol. Three major protocols are used in embedded systems; the I2C, the serial peripheral interface (SPI), and the universal asynchronous receiver transmitter. The I2C is the popular communication standard for the RPi digital sensor interface. The basic I2C setup using real-time sensors and RPi is shown in the diagram below. The use of unmanned aircraft will greatly aid farming. There will be an increase in drone data to identify crop varieties, classify weeds, and assess pest- damaged crops in the future. Precision agricultural spraying can be improved with more intelligence, allowing farmers to use fewer pesticides and reduce human exposure to potentially hazardous compounds while increasing crop yields. Figure 3, in terms of communication, uses a synchronous, two-line, half-duplex configuration. DS18B20 and DHT11 sensors are used as slaves by the RPi in this experiment. Inability to talk with and control synchronous master and slave devices, they use the serial communication line and the serial countdown. The master–slave configuration of sensors and RPi is shown in Figure 3. Each I2C bus device has a seven-bit hardware address with a transmission rate of 100 kbps. The I2C can handle up to 127 devices using two lines named SCL and SDA with a seven-bit address. The first byte contains a seven-bit address and a read/write bit followed by the actual data during transmission. It is a one-wire protocol accessible through the GPIO pins of RPi. It operates in a synchronous two-line half-duplex mode of communication. Raspberry Pi serves as the master device in this research, with sensors such as the DS18B20 and DHT11 serving as slaves. They can 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

6. 6 SELVARAJ ET AL. F I G U R E 2 The five-layer architecture of real-time agricultural field monitoring and smart irrigation architecture using Internet of Things and Quadrotor unmanned aerial vehicles. FCM, Firebase Cloud Message; HTTP, hypertext transfer protocol; SCLH, serial clock line high; SDAH, serial data line high; VDD, voltage drain drain; XMPP, extensible messaging and presence protocol F I G U R E 3 The inter-integrated circuit communication interface between sensors and Raspberry Pi. SCL, serial clock line; SDA, serial data line interact and control synchronous master and slave devices via two lines: SDA and SCL. The parameter that can handle up to 127 devices using 196 two lines called SCL and SDA and a seven-bit address, with IDs are 3 bytes long and contain three fields: the manufacture ID, the device ID, and the die revision number, each with a length of 12/9/3 bits. The device ID is a 7-bit physical address of the sensor; it is used to distinguish the sensor data received through the I2C line. The transfer diagram indi- cates in Figure 4 that each byte on the SDA line is 8-bit length followed by an acknowledgment signal. The number of bytes sent to each transmission is not limited, and data transmission continues until the slave is ready for another byte of clock line SCL and releases. The device ID of sensors is 3-bytes long with three fields; manufacture ID, device ID, and die revision 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

7. SELVARAJ ET AL. 7 F I G U R E 4 Data transfer diagram in the inter-integrated circuit protocol (Ref. I2C datasheet no. UM10204). ACK, acknowledgment; MSB, most-significant bit; SCL, serial clock line; SDA, serial data line number with 12/9/3 bit long, respectively. A device ID is a 7-bit physical address of the sensor; it is used to differentiate the sensor data received through the I2C line. 3.2 Physical layer The physical layer or perception layer hosting intelligent IoT includes various devices such as sensors, actuators, machines, and motors. The sensor devices gather real-time data; the actuators are used to automate electrical electronic equipment. Machines and appliances are part of this layer linked to the other two utilities. The working and role of the numerous sensors used in this proposal will be demonstrated in-depth in the next sections. 3.2.1 DS18B20 Temperature sensor Plants require four things; light, water, soil, and air. To grow healthy plants, the essential component is water impact. The DS 18B20 is a unique digital temperature sensor with 64-bit serial data output and one-wire communication compatibility. It has an internal configurable ADC with 9 and 12-bit resolution. The system can control multiple devices distributed over a large area because the RPi computer integrates with serial communication protocols like I2C and SPI. Here the temperature sensor is configured for 12-bit resolution and connected to the I2C pin of RPi; the 4th general-purpose input–output pin (GPIO-4) is configured to connect the digital temperature sensor. This unit measures the atmosphere temperature from −55 to +125˚C with an accuracy of ±0.5˚C for −10˚C to +85˚C. The interfacing circuit of DS18B20 with RPi is shown below. In Figure 5, the complete interfacing circuit of RPi and DS18B20 temperature sensor. To sustain the higher leakage current, power the unit from GPIO pins of RPi. A water- proof model of the DS18B20 sensor is selected, and it is best appropriate for this hydro project. Some of the proto- F I G U R E 5 Interfacing circuit of Raspberry Pi with DS18B20 temperature sensor. DQ, digital data output; GND, digital ground; GPIO, general-purpose input–output; SCL, serial clock line; SDA, serial data line; VCC; voltage collector collector; VDD, voltage drain drain; VSS, voltage source source cols used in embedded systems are conventional protocols for serial peripheral interfaces, including SPI, I2C, universal synchronous/asynchronous receiver/transmitter, and control area network. The RPi CPU works as the master and other sensors as slaves. A pull-up resistor with 470 Ω is used between digital output and VDD pin; digital output pins are a tri-state or open- drain port used to help the master unit identify the 1-Wire bus temperature conversions. Each DS18B20 has a unique 64-bit code stored in ROM. The least significant 8 bits of the ROM code contain the DS18B20’s 1-Wire hardware address stating 28 h; this address is used to provide an error-free communication between master and slave as given below. Temp_device_folder = glob.glob (′ ∕sys∕bus∕w1∕devices∕′ +′ 28∗′ ) [0] (1.1) 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

8. 8 SELVARAJ ET AL. device_file = device_folder +′ ∕w1_slave′ (1.2) Equations 1.1 and 1.2 are used to detect the data from the I2C sensor connected to the RPi. The I2C protocol layer continuously checks for the data packet from any sensors with starting address of 28 h. The following 48 bits have a unique serial number followed by an 8-bit error correction code. Equation 1.1 finds the slave devices with an address 28 h and reads the data packet using Equation 1.2. The data from the I2C sensor connected to the RPi is detected using these equations. The I2C protocol layer executes continuous data packet checking for sensors with starting addresses of 28 h, and higher An 8-bit error-correcting code follows the unique serial number in the remaining 48 bits. The resolution of DS18B20 is improved using the Equation 2; the values for the register named Count Per˚C (Rcount) and Count Remain (Crem) are used in this equation. Temp = Temp𝑎𝑑𝑐 − 0.25 + ( 𝑅count − 𝐶count / 𝑅count ) (2) In Equation 2, Tempadc is the digital equivalent of temperature read from ADC register, Rcount and Crem are the count values of the ADC registers accessible through the I2C protocol. The data received from this unit is hosted to the firebase database in real-time. The end-user or framer may see and analyze data from their portable gadget such as smartphones and personal digital assistants. The temperature and humidity control systems are required to rate plant development, production capacity, and product quality. Various experimental experiments were conducted over time. A common application for I2C is reading information from sensors and controlling certain components via a master–slave bus protocol. The RPi provides the master, and all of the slaves are attached to it via a single bus. The information in this collection is used to accomplish the effects depicted in the following diagram. Figure 6 displays the temperature readings acquired through the DS18B20 sensor from March to June 2021. The created system constantly monitors the above characteristics and transmits them to the firebase cloud without delay. These parameters are beneficial for the farmers, particularly for greenhouse platforms to schedule drip irrigation. 3.2.2 DHT11 Humidity sensor Temperature or humidity imbalances may have a range of harmful effects on plants and possibly cause harvests to be squandered. Moisture is the actual water vapor concentration percentage at a given temperature and pressure. It directly affects plant water relations and indirectly affects leaf development, disease likelihood, and economic yield. DHT11 has a surface-mounted negative temperature coefficient thermistor and resistive moisture sensor. It transforms thermistor and humidity sensor resistance data into digital temperature and relative humidity measurements. The relative humidity of a sensor is defined by the equation, which is as follows: 𝑅Hum = 𝑅Ref_Hum [( 𝐶Cap − 𝐶_Cap_bulk ) ∕Sensitivity ] + Temp_Dep (3) According to the humidity sensor used, there is a different way to calculate the temperature dependence (Temp_Dep) of the relative humidity measurement (RHum). The capacitance of the sensor is represented by CCap and bulk capacity using C_Cap_bulk. In cultivating healthy plants, water impact is essential. One-wire communication and 64-bit serial data output make the DS 18B20 a truly unique digital temperature sensor from Equation 3, for a sensor’s relative humidity, is calculated. The temperature-dependent measurement of relative humidity can then be obtained. The sensitivity of the sensor is calibrated using the equation: Sensitivity = ( Cap_𝑅95Hum − Cap_𝑅10Hum ) ∕85% (4) Equation 4 indicated that sensitivity is the measure of the difference of capacitance of the sensor at 95% (R95Hum) and 10% of relative humidity R10Hum. Figure 7 shows the real-time sensor interfacing with RPi, connecting digital sensor output to GPIO 3 using one wire communication technique. The sampling rate of DHT11 is 1 Hz or one reading per second, and the working voltage is 3–5 volts of 2.5 mA. It can measure relative humidity from 20 to 90% and temperature from 0 to 50 ˚C. Due to its limited temperature range, DHT11 is dedicated to detecting humidity, and DS18B20 is available in sealed packaging with an exten- sive temperature range. The DHT11 humidity value is sent to the firebase console and is seen in the farmer’s mobile unit using the IoT infrastructure. The real-time humidity readings from the DHT11 sensor of field 1 are displayed in Figure 8. Many variables, including humidity, produce difficulties like foliar and root diseases, grade loss, etc. So more pesticides are needed to control disease, and plants have weak, stretched growth, making them unattractive. Low humidity stunts plant growth, causing crops to mature slowly. Low humidity affects quality, boosts production costs, and reduces profits. The use of unmanned aircraft will greatly aid farming. There will be an increase in the use of drone data to identify crop varieties, classify weeds, and assess pest-damaged crops in the future. Precision agricultural spraying can be improved with more intelligence, allowing farmers to use fewer pesticides and reduce human exposure to potentially hazardous compounds while increasing crop yields. 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

9. SELVARAJ ET AL. 9 F I G U R E 6 Real-time temperature readings from field 1 from March to June 2021 F I G U R E 7 Interfacing circuit of Raspberry Pi with DHT11humidity sensor. DQ, digital data output; GND, digital ground; GPIO, general-purpose input–output; SCL, serial clock line; SDA, serial data line; VCC; voltage collector collector; VDD, voltage drain drain; VSS, voltage source source 3.2.3 Soil moisture and salinity sensor Soil moisture management is an essential farming component for improving productivity and farmers’ commercial position. Farmer’s ultimate goal is to improve water storage and moisture efficiency. Unfortunately, rural society still has a diffi- cult job and needs additional technical assistance with intel- F I G U R E 8 Real-time humidity readings from DHT11 from field 1 from March to June 2021 ligent sensors and automated irrigation management systems. The World Meteorological Organization stated soil moisture as one of the key climatic variables. Routine soil test- ing can determine the salinity levels in soil and recommend actions to rectify the specific salinity problem in the ground. With an increase in salinity of soils, plants are less capable of getting as much water from the soil. The pH, electrical 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

10. 10 SELVARAJ ET AL. F I G U R E 9 Interfacing circuit of Raspberry Pi with soil moisture and salinity measurement. ADC, analog to digital converter; GPIO, general-purpose input–output; SCL, serial clock line; SDA, serial data line; VDD, voltage drain drain; VSS, voltage source source conductivity, and water-soluble levels of the soil are determined by this experiment. This research suggested a tai- lored electrical sensor to detect soil moisture using resistive and conductive characteristics to solve problems in the paddy field. The system developed for experimental analysis is given below. The unique hardware configuration in Figure 9 is built using high-precision ADC MCP 3008 with a 200kSPS sampling rate. It is an 8 bit 10, channel ADC with I2C protocol; it is the most suitable analog interface for the RPi platform. The ADC is configured with software SPI protocol. Hardware SPI is less flexible and works with specific pins of RPi. Land conductivity and resistivity are tested using 5-cm apart copper plates 25-cm long. The salinity of the water is a critical met- ric to consider when attempting to gauge its overall quality. Water’s salinity is determined by the number of salts that have been dissolved. Typically, this quantification is computed by dividing or parts per million. The water’s conductivity has been measured with a network that helps or contacts the per- meability sensor to get this value. One plate (P1) is driven by DC voltage, and the voltage measure on Plate P2 is directly proportional to soil conductivity in both salt and water content. The ADC’s output is connected to GPIO 4 of RPi; this I2C protocol configures the sensor node as a slave. Figure 9 shows how to set up the ADC’s SPI protocol as the best analogue interface for the RPi platform. Because the RPi has only specific pins for SPI, hardware SPI is the only option. Salt and water content directly affect the voltage measured on Plate P2. The RPi’s GPIO 4 is connected to the ADC’s output, and the sensor node is configured as a slave in the I2C protocol. The moisture content in soil is determined using the equation: 𝑀c = ( 𝑊m_soil − 𝑊d_soil ) ∕𝑊d_soil (5) Here, Wm_soil is the weight of moist soil and Wd_soil is the weight of dry soil taken from the field. Furthermore, the water content in soil is calculated using the formula given below. 𝑊depth = 𝑅b_dens [ 𝑊percent 100 ] 𝑆depth (6) In Equation 6, Rb_dens, Wpercent and Sdepth representing relative bulk density, percentage of water content, and soil depth, respectively. It is necessary to use an analog-to-digital converter MCP3008 to read the voltage on the RPi. The voltage can be calculated using the following formula: 𝑉out = ( ADCout∕1, 023 ) 𝑉in (7) In Equation 7, the value Vout will depend on the input voltage of the sensor Vin, here Vin is set with 5 volts. The resolution of ADC MCP 3008 is 10 bits, and for conversion, ADC output is divided with a value of 1,023. The experimental setup for soil surface moisture is approximately 10–15 cm, 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

11. SELVARAJ ET AL. 11 and the root-soil water is monitored using long copper rode according to the plants up to 200 cm. The proposed framework measures air humidity coupled with soil moisture and salinity and is displayed in the firebase console. In general, the decision for irrigation relies on soil moisture content. The upper humidity is set to 90 and 75% as the lower humidity to ensure error-free operation. The designed irrigation water depth is given below. Ir g_depth = Sm (𝑖) (8) In Equation 8, Ir g_depth is the required irrigation depth in centimeters and Sm(i) is the soil moisture measured at ith an instant of irrigation. For the précised irrigation, the amount of water required is defined as: IrVol = 0.1 × Sm (𝑖) × Sd × Pw (Ul − Ll)∕ξ (9) Here, I rVol is the required volume of irrigation in millime- ters, Sd is soil density, Pw is a percentage of wet soil, ξ is coefficient of drip irrigation, Ul and Ll is the upper and lower irrigation level at ith instant, respectively. By installing this design, every interval detected parameter is updated, and if any of the earlier parameters are substantially changed, they are instantly notified by message. One of the disadvantages of the previous method was measuring the moisture in topsoil layers; it was addressed by using this long copper rod experiment. 3.2.4 Wind speed sensor This section examines the effect of wind and rain on agricultural farming and crop production using IoT sensors. The wind requirement changes depending on the crop type, and wind direction and velocity have a considerable effect on crop growth. For example, wind enhances ethylene production and nitrogen concentration in barley (Hordeum vulgare L.) and rice (Oryza sativa L.), lowering the rice’s gibberellic acid level. As shown in Figure 10, the developed equipment is evaluated for the laboratory’s best adjustment and calibration pro- cedure. Anemometer is the right instrument for measuring wind speed; this research proposes a customized, low-cost tool using an SMPS/PC Fan. A PC fan is brushless, and when the rotation speed varies, it produces electrical AC voltage. It has a rotation per minute (RPM) of 2,000–2,500 at 12 V-0.5 amperes. The digital signal oscilloscope shows the AC voltage generated by the fan in real time. The rotation speed changes by altering the power signal and calibrating between 1 and 12 Volt DC input voltage. Corresponding volt- ages from the wind gauge are measured in digital multimeter with maximum accuracy. The following graph explores F I G U R E 1 0 Laboratory set-up for the measurement of the wind velocity and AC voltage output. (1) Tektronix TBS11023B/100Mhz Digital Signal Oscilloscope. (2) Wind speed measurement using PC fan. (3) Keithley 223A 30V/2Atripple channel power supply. (4) Supporting stand. (5) Sonel CMM-11 Digital multimeter F I G U R E 1 1 Rotation per minute (RPM) vs. AC output voltage of wind gauge sensor the correlation between RPM and the voltage in laboratory configuration. In Figure 11, the graph shows the speed of rotation (RPM) vs. output voltage ratio. The AC voltage from the fan output is connected to the MCP3008 ADC’s first channel (A0). The voltage generated by the fan is proportional to the RPM and is converted to wind speed with certain programming methods. The three control signals master in slave out (MISO), master out slave in (MOSI), and serial clock (SCK) signals provided the synchronization between MCP3008 and RPi. A motor can be used quite well as a speed sensor, despite the practical problem of not calibrating it right. The motor’s 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

12. 12 SELVARAJ ET AL. F I G U R E 1 2 Interfacing circuit of Raspberry Pi (RPi) with wind speed measurement system. ADC, analog to digital converter; GPIO, general-purpose input–output; SCL, serial clock line; SDA, serial data line; VDD, voltage drain drain; VSS, voltage source source internal resistance does not influence the response of the output because this shifts the ratio of the voltage divider, which gets canceled out by the “zero” value. Generally, calibrating this setup gives some problems, then turning a motor into a wind speed sensor works quite well. In Figure 12, ADC MCP3008 is used to measure wind sensor voltage, and it acts as an interface between RPi and sensor. Naturally, an analogue output does not immediately correlate with wind speed. Therefore, a correlation function is applied to link analogue inputs to real-world data by sampling the data for 10–15 min. The equation below shows the correlation function of speed and output voltage of the wind sensor. 𝑌0 − 𝑌1 = 𝑀𝑋0 − 𝑋1 (10) In Equation 10, M is the slope of input and output, Xi and Yi are the analogue output voltage and speed of rotation, respectively. The experimental setup and results in Figure 13 showed that a CPU fan could detect the wind speed accurately without any further hardware changes and with an appropriate tuning mechanism. Equation 10 is correlated speed and output voltage of the wind sensor. Wind direction and speed affect crop growth significantly. Wind promotes atmospheric turbulence, boosting the availability of carbon dioxide to plants, resulting in higher levels of photosynthesis. The wind affects the hor- mone balance and promotes ethylene generation in crops like rice and barley. F I G U R E 1 3 Real-time wind speed readings for a period of March to June 2021 3.2.5 Rain gauge sensor The rain gauge is built using a Hc Sr 04 ultrasonic sensor. It is a noncontact sensor with a 2 mm accuracy range of 2–400 cm. The Hc Sr 04 sensor is ideal for monitoring the rain gauge. The experimental setup has a 5-cm radius glass tube; the distance between sensor and water decreases if tube water rises. The timing diagram of this process is shown in Figure 14. 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

13. SELVARAJ ET AL. 13 F I G U R E 1 4 (a) Timing diagram of Hc SR 04 Ultrasonic Sensor (Ref. HC-SR04 datasheet). (b) Interfacing circuit of Raspberry Pi with rain gauge sensor using Hc SR 04. GPIO, general-purpose input–output; SCL, serial clock line; SDA, serial data line; VDD, voltage drain drain; VSS, voltage source source To start measuring, the SR04’s Trig and pin must receive a high pulse for 10 us. The sensor will then fire an eight- cycle ultrasonic burst at 40 kHz and wait for the reflection. Any ultrasonic detected by the sensor raises the Echo pin and delays proportionally. To find the distance, measure the signal width at the echo pin. Sound travels through air at roughly 344 m s–1; therefore, multiply the time it takes for the sound wave to return by 344 to obtain the total round-trip distance. Divide the round-trip distance in half to get the distance to the item. Distance = (Speed of sound × time) ∕2 (11) The formula for the speed of sound in the air when temperature and humidity are taken into consideration is: 𝐶 = 331.4 + (0.606𝑇 + 0.0124𝐻) (12) where C is the sound speed in meters per second, at 0 ˚C and 0% humidity, the speed of sound is 331.4 m s–1. The letter T stands for temperature, and H denotes the relative humidity percentage. The quantity of water in the tube is represented by the distances calculated by Equation 5. This value denotes the unit of rainfall in that geographical region. The physical and geographical factors listed above will help farmers decide and take measures on specific agricultural farming. 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

14. 14 SELVARAJ ET AL. ALGORITHM Smart irrigation using sensors and IoT platform 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

15. SELVARAJ ET AL. 15 F I G U R E 1 5 The proposed architecture of the Quadrotor unmanned aerial vehicles (Q-UAV) filed monitoring system. APN, AWS Partner Network; PDA, personal digital assistant; XMPP, extensible messaging and presence protocol Algorithm 1 has been scheduled into three steps: initializa- tion and atmospheric and soil parameters measurement. While the system is initializing, it will set the threshold values for each sensor following the settings that have been made. If the mode is manual, the system will wait for commands from the user’s side, which is received over the cloud interface. When in automatic mode, the system will read the I2C data from the GPIO4 line and extract the temperature and humidity values from the data stream using MACADDR. Because of the intelligence provided to the RPi through the Python IDLE, the smart irrigation system has been controlled efficiently without manual intervention. The IDLE identified the DS18B20 and DHT11 using the starting address 28xx:yy: and 84xx:yy: respectively. Equations 1.1 and 1.2 are used to read the real- time temperature TRT and humidity HRT. When the relative humidity is greater than or equal to threshold humidity (HRT ≥ HTH) and (TRT ≥ TTH) the drip irrigation has been activated by RPI. The soil parameters are interfaced with the cloud server through an SPI line and are stored on the cloud server. Farm- ers can get this information through smartphones and personal digital assistants. In addition, farmers can use their cell phones to monitor and regulate the status of the motor. 4 UNMANNED ARIAL VEHICLES FOR REAL-TIME AGRICULTURE FIELD MONITORING Because of the changes in the agriculture industry, Quad- copter unmanned aerial vehicles (UAVs) have emerged as one F I G U R E 1 6 Real-time smart irrigation and monitoring system using Internet of Things and Quadrotor unmanned aerial vehicles architecture. (1) 12V/125Watt solar panel. (2) 12v/7Ah battery. (3) RPI 4B computer. (4) DHT 11 sensor. (5) Battery charge indicator. (6) DS18B20 sensor. (7) Wind gauge. (8) Soil salinity and moisture sensor. (9) Supporting stand 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

16. 16 SELVARAJ ET AL. TA B L E 1 DHT11 measurement vs. existing sensor SI no. Dorigin Dresearch Perror 1 85.56 84.94 0.72 2 87.11 87.9 0.89 3 82.45 81.38 1.29 4 92.67 91.9 0.83 5 89.1 90.21 1.23 6 93.44 93.88 0.46 7 87.58 87.9 0.36 8 84.55 84 0.65 9 86.98 86.33 0.74 10 83.52 83.89 0.44 Note. Dorigin, original data; Dresearch, research data; Perror, percentage of error; SI, serial number. TA B L E 2 DS18B20 temperature vs. mercury-based sensor SI no. Dorigin Dresearch Perror 1 33.23 32.89 1.02 2 31.45 31.32 0.41 3 31.98 31.88 0.31 4 33.55 33.05 1.49 5 32.12 31.95 0.53 6 32.94 32.89 0.15 7 34.21 33.58 1.84 8 33.98 33.45 1.56 9 33.94 33.32 1.83 10 33.58 33.11 1.4 Note. Dorigin, original data; Dresearch, research data; Perror, percentage of error; SI, serial number. of the most widely recognized and fascinating technologies in the world right now. Farmers are using smart sensors, and UAVs are always required to obtain accurate and up-to-date information about crop health and the presence of insecti- cides in remote areas. It is an essential part of their business, especially in large farming areas and small-scale operations. The proposed vision-based Q-UAV for IoT applications is discussed in the following section. Implementation of the proposed Q-UAV architecture with RPi as the central controlling station for computer vision and flight assistance is shown in Figure 15. The Q-UAV used for this research and RPi unit will have made it possible for real- time HD image and video streaming to the Firebase cloud. The UAV is installed with RPi is, an eight-megapixel camera serial interface camera. The end-user can view the real- time video through an application installed on their Android mobile device, as well as from a cloud database accessed remotely through the IoT. The IoT can offer new cost and process advantages to machine vision and open the way for TA B L E 3 Proposed wind speed vs. anemometer SI no. RPM Dorigin Dresearch 1 154 0.79 0.82 2 250 1.29 1.34 3 310 1.63 1.7 4 445 2.27 2.36 5 520 2.65 2.76 6 639 3.36 3.5 7 734 3.84 4 8 850 4.34 4.52 9 945 4.82 5.02 10 1,056 5.39 5.61 Note. Dorigin, original data; Dresearch, research data; RPM, rotation per minute; SI, serial number. TA B L E 4 Proposed soil moisture vs. existing machine SI no. Dorigin Dresearch Perror 1 45 47 4.26 2 47 48 2.08 3 53 55 3.64 4 58 60 3.33 5 64 65 1.54 6 49 51 3.92 7 65 66 1.52 8 48 49 2.04 9 66 67 1.49 10 59 59 0 Note. Dorigin, original data; Dresearch, research data; Perror, percentage of error; SI, serial number. the integration of computer vision into inspection systems. Using UAVs, conventional agricultural techniques may close the gap left by human error and inefficiency. The Q-UAVs are expected to provide a variety of services. It is more chal- lenging to obtain aerial imagery; manned aircraft or satellites deliver the message. The Q-UAV technology is eliminated all ambiguity or guessing and concentrates on precise and trust- worthy information. Earlier, remote monitoring and sensor data interfacing were restricted. Because advancing with embedded interfaces and IoT in SoC chips, remote data monitoring has been feasible using cloud services like Thingspeak, Firebase, and the pri- vate cloud. Due to broad communication protocol support and video interfacing with operating system support, RPi is chosen as the central controller for UAV and sensor interfacing. The data from sensors send to google firebase using RPi, DS18B20, DHT11 and wind speed, soil salinity, and moisture. Besides this, images of files collected by Q-UAVs are sent to the cloud console without delay. 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

17. SELVARAJ ET AL. 17 F I G U R E 1 7 Experimental results for observed and actual data: (a) humidity and (b) temperature F I G U R E 1 8 Experimental results for observed and actual data: (a) wind speed and (b) soil moisture 5 RESULTS AND DISCUSSION In this research, the primary goal is to collect the physical parameters of a farming land using sensors and to use the data collected by the sensors, along with live images from the land, to develop a challengeable framework for smart agricultural fields. When combined with computer vision, this architecture improves the accuracy of soil and atmospheric parameter measurement. As demonstrated in the experiment, a good estimation of the soil as mentioned above parameters with a Q-UAV system can be used for optimum irrigation with efficient natural rain and resources use. As described in Section 3, a field data collection node has been deployed in the agricultural land with one unit for 0.2 ha of land. The data is collected at a cloud server using the web services, and this data is analyzed using the farmers on their smartphones. Further, a responsive firebase cloud interface has been developed for real-time monitoring, data visualization, decision support, and drip irrigation scheduling. A photograph of a real-time monitoring and irrigation system with a solar power backup is shown in Figure 16. The newly built system with low power consumption and envi- ronmentally friendly architecture has been implemented. The proposed technology is tested in various fields to determine its suitability and accuracy for practical application. The implementation considers some of the essential parameters listed above, and they are computed both on analog sensors and on a 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

18. 18 SELVARAJ ET AL. cloud platform. The following equation illustrates error analysis by comparing research data from the proposed system and the original data. 𝑃error = [ 𝐷origin − 𝐷research 𝐷origin ] 100 (13) In Equation 13, research classifies data collected by sensors as Research data (Dresearch) and original data (Dorigin). The suggested system’s performance is tested numerous times to assure correctness. The next part analyzes this research’s accuracy and error (Perror) in depth. The next sections exam- ined the research output for research parameters using existing meters and the proposed method, together with their error levels. Tests measuring humidity using existing equipment and the proposed approach are shown in Table 1, along with a percentage of detected errors. Observations from the preceding inves- tigation have revealed that the average error probability Perror is 1.09%, and the maximum error is up to 1.29%. The results indicated that 98.71% of the accuracy of the developed hardware had been obtained after being verified in various fields. Table 2 shows the results of the temperature measurement using the DS18B20 sensor. To ensure the correctness of the proposed system, the performance of the proposed system is tested numerous times. The difference between actual data and research data is investigated and presented. It has achieved a notable accuracy of 98.16%, which is impressive. Table 3 shows the results of tests detecting wind speed in km h–1, using anemometers and the suggested system, together with their error values. The accuracy of this research obtained as 94.39%, while the average error is 3.16%. The pro- duced system’s efficacy is fulfilled with the least amount of design work and the lowest possible cost. There is 95.74% efficacy of soil moisture in this experiment, and this research output is sufficient to meet the goals of this research. Among other things, the Perror at different observations for different parameters such as humidity, temperature, wind speed, and soil moisture are displayed in each of the four tables in the preceding part of this paper. Temperature is calculated in degrees Celsius; humidity is reported in percentage units. Tables 1 and 2 computed the Perror of each parameter using Equation 13. It has been observed that the average real-time humidity error is 1.09% and maximum error up to 1.29%. Table 2 and Figure 17b shows that the temperature sensor has 0.68 and 1.29% of average and maximum error, respectively. In general, wind speed is expressed in kilometers per hour (km h–1). The percentage of wind velocity and soil moisture errors is shown in Tables 3 and 4 and visually shown in Figure 18. It produces an average error of 3.93 and 2.65% and a maximum error of 4.11 and 4.35. F I G U R E 1 9 Experimental results plotted with percentage of error (Perror) The foregoing study has concluded that the proposed method has less risk of error and offers a high level of precision and feasibility than any irrigation management system using IoT and sensors (Figure 19). Afterward, the data can be evaluated and securely saved in the firebase cloud, where authorized persons or farmers can access it at any moment for additional purposes. The developed system operates in two modes, namely, automatic mode and manual mode. Choos- ing an irrigation system in manual mode is dictated by the user’s assessment of the projected soil and atmospheric conditions. In addition, the user can monitor the fields using the Q-UAV through a cloud-based interface. While in auto mode, a user can specify the threshold values for temperature, soil moisture, salinity, and other variables. The system automatically adjusts the irrigation depending on the sensor information. The proposed system is as smart as the precision of the sensor measurements it uses when it comes to smartness. Hourly field data for temperature, humidity, soil moisture, and wind speed will be collected for 4 mo from March to June 2021 to evaluate the accuracy of the model’s prediction. In Section 3.2, hourly data for the previous 4 mo has been averaged each day, and this information is discussed. 6 CONCLUSION The environmental factors, including temperature, humidity, wind speed, soil moisture salinity, are the most significant parameters in a smart irrigation system. With improvement in technology, a UAV-based monitoring system will lower the labor cost and enhance crop yield by taking real-time 14350645, 2023, 1, Downloaded from https://acsess.onlinelibrary.wiley.com/doi/10.1002/agj2.21061 by INASP/HINARI - BOTSWANA, Wiley Online Library on [16/02/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

19. SELVARAJ ET AL. 19 decision-making mechanisms. This designed framework with smart sensors and computer vision has accurately monitored the ambient factors. These values are available in the user front end and the firebase console without IoT architecture latency. The open-source software and RPi platform is the best ideal for IoT and AI applications; it decreases the man- ufacturing and maintenance cost of the product generated. The auto mode setup makes the smart irrigation system more flexible to the farmers and innovative. The average variation between real data and observed humidity, temperature, wind speed, and soil moisture is 1.09, 0.68, 3.93, and 2.65%, respectively. These results and the feasibility thought of the suggested strategy are presented. This irrigation framework and Q-UAV design can be deployed in any farming sector like live- stock and plants with solar power. AC K N OW L E D G M E N T S The authors would like to thank Department of Science and Technology (DST), New Delhi, India, for the funding to carry out the Research work - DST/TDT/AGRO-20/2019 & 22-01- 2020 from Karpagam Academy of Higher Education, Coim- batore, India, and the dataset collection has been supported from Botswana International University of Science and Tech- nology (BIUST), Botswana. AU T H O R C O N T R I B U T I O N S Rajalakshmi Selvaraj: Conceptualization; Methodology; Writing – original draft; Writing – review & editing. Venu Madhav Kuthadi: Software; Validation. S Baskar: Data curation; Formal analysis C O N F L I C T O F I N T E R E S T The authors declare no conflicts of interest. O RC I D Rajalakshmi Selvaraj https://orcid.org/0000-0002-5571- 5316 R E F E R E N C E S Albuquerque, C. K., Polimante, S., Torre-Neto, A., & Prati, R. C. (2020). Water spray detection for smart irrigation systems with Mask R- CNN and UAV footage. 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Real-time agricultural field monitoring and smart irrigation architecture using the internet of things and quadrotor unmanned aerial vehicles

Real-time agricultural field monitoring and smart irrigation architecture using the internet of things and quadrotor unmanned aerial vehicles

Real-time agricultural field monitoring and smart irrigation architecture using the internet of things and quadrotor unmanned aerial vehicles

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