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Optimizing AI Applications for Sustainable Agriculture

Edited by Roheet Bhatnagar, Chandan Kumar Panda and Mahmoud Yasin Shams
Copyright: 2025   |   Expected Pub Date:2025/07/30
ISBN: 9781394287239  |  Hardcover  |  
562 pages

One Line Description
Embrace the future of sustainable food production with this comprehensive guide that explores how artificial intelligence and emerging technologies are revolutionizing agriculture.

Audience
Researchers, computer and agricultural scientists, farmers, and policymakers looking to leverage the potential of artificial intelligence and machine learning for the benefit of farmers.

Description
In an era marked by climate change, resource depletion, and population growth, innovation is not a luxury—it is a necessity. Integrating AI into agricultural practices offers a promising solution. From precision farming and crop monitoring to predictive analytics and decision support systems, AI has the potential to revolutionize how we grow, manage, and distribute food. This book is a comprehensive guide that delves into the transformative potential of artificial intelligence and emerging technologies in the field of agriculture. An in-depth exploration of various AI technologies, such as machine learning, deep learning, natural language processing, and computer vision, will demonstrate the wide applications these tools have for agricultural practices. It covers emerging technologies like the Internet of Things, drones, precision farming, and agro-technology. The primary focus is on how these technologies can enhance sustainability in agriculture by improving crop yields, reducing water consumption, minimizing chemical use, and promoting eco-friendly farming practices. This essential guide will give readers a deep understanding of how cutting-edge technology can be harnessed to create a more sustainable future for agriculture.
Readers will find the volume:
• Dives into the latest research and innovations in AI and emerging technologies that are transforming agricultural practices;
• Provides real-world examples and case studies that show how these technologies can be implemented in farming;
• Explores how these modern technologies align with global sustainability goals and how they can be integrated into national strategies;
• Introduces the role of AI and emerging technologies in promoting sustainable agricultural practices that protect the environment.

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Author / Editor Details
Roheet Bhatnagar, PhD is a Professor in the Department of Computer Science and Engineering at Manipal University, Jaipur, Rajasthan, India with over 22 years of experience. He has published more than 100 research papers in reputed conferences and journals and edited five books. His research focuses on soft computing, data structure, and software engineering.

Chandan Kumar Panda, PhD is an Assistant Professor at Bihar Agricultural University, Sabour, Bihar, India with over eight years of research and teaching experience. He has published three books, 16 book chapters, and more than 50 research papers in international journals and conferences. He is an acclaimed researcher in ICT in the agriculture sector. His research interests include agricultural extension, rural development, and information and communication technology in agriculture.

Mahmoud Yasin Shams, PhD is an Associate Professor of Machine Learning and Information Retrieval in the School of Artificial Intelligence, Kafrelsheikh University, Kafr el-Sheikh, Egypt. With over 70 papers and conference presentations published in top-tier journals he has made significant contributions to the field. He specializes in artificial intelligence, machine learning, pattern recognition, and classification.

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Table of Contents
Preface
Part I: Artificial Intelligence-Assisted Sustainable Agriculture
1. AI and Emerging Technologies for Precision Agriculture: A Survey
Brajesh Kumar Khare
1.1 Introduction
1.2 Precision Agriculture
1.3 Artificial Intelligence
1.3.1 Role of AI in Agriculture
1.4 Internet of Things (IoT)
1.4.1 Basics of IoT in Agriculture
1.4.2 Role of IoT
1.5 Blockchain Technology
1.6 Technologies Used in Smart Farming
1.6.1 Global Positioning System (GPS)
1.6.2 Sensor Technologies
1.6.3 Variable Rate Technology and Grid Soil Sampling
1.6.4 Geographic Information System (GIS)
1.6.5 Crop Management
1.6.6 Soil and Plant Sensors
1.6.7 Yield Monitor
1.7 Challenges
1.8 Future Research
1.9 Conclusion
References
2. AI-Enabled Framework for Sustainable Agriculture Practices
Yukti Batra, Suman Bhatia and Ankit Verma
2.1 Introduction
2.2 Sustainable Agriculture Imperatives
2.2.1 Environmental Degradation
2.2.2 Biodiversity Loss
2.2.3 Climate Change Impacts
2.2.4 Resource Scarcity
2.2.5 Food Security and Economic Stability
2.2.6 Public Health Concerns
2.2.7 Social Equity and Rural Livelihoods
2.2.8 Global Food Shortage Concerns
2.2.9 Empowerment and Awareness
2.3 Social Relevance of Sustainable Practices in Agriculture
2.3.1 Livelihood Security
2.3.2 Community Health and Well-Being
2.3.3 Social Equity and Inclusion
2.3.4 Rural Empowerment and Resilience
2.4 Sustainable Agriculture Indicators
2.4.1 Food Grain Productivity
2.4.2 Population Density
2.4.3 Cropping Intensity
2.5 Sustainable Agriculture Practices Followed Till Date
2.5.1 Agroforestry
2.5.2 Integrated Pest Management (IPM)
2.5.3 Crop Rotation
2.5.4 Cover Cropping
2.5.5 Organic Farming
2.5.6 No-Till Farming
2.6 AI-Enabled Conceptual Framework
2.6.1 Perception from Environment Using IoT Sensors
2.6.1.1 Remote Sensing
2.6.1.2 IoT Sensors
2.6.2 Data Storage
2.6.3 Data Processing
2.6.4 Training and Testing by ML Models
2.7 Applications of Artificial Intelligence in Agriculture
2.8 Challenges and Barriers to Sustainable Agriculture
2.8.1 Theoretical Obstacles
2.8.2 Methodological Obstacles
2.8.3 Personal Obstacles
2.8.4 Practical Obstacles
2.9 Future Directions
2.10 Conclusion
References
3. The Impact of Artificial Intelligence on Agriculture: Revolutionizing Efficiency and Sustainability
Santhiya S., P. Jayadharshini, N. Abinaya, Sharmila C., Srigha S. and Sruthi K.
Applications
3.1 Introduction
3.2 Precision Farming
3.2.1 Data Collection and Analytics
3.2.2 Disease Detection
3.2.3 Yield Production and Optimization
3.2.4 Precision Irrigation
3.3 Crop Monitoring
3.3.1 Remote Sensing and Satellite Imagery
3.3.2 Drones
3.3.3 Computer Vision and Image Analysis
3.3.4 Sensor Network and IoT
3.3.5 Weed Detection Management
3.4 AI in Aquaculture
3.4.1 Monitoring Water Quality
3.4.2 Feed Management
3.4.3 Breeding Technique
3.4.4 Autonomous Systems and Market Optimization
3.5 Predictive Analysis
3.5.1 Irrigation Optimization
3.5.2 Supply Chain Management
3.5.3 Weather and Climate Modeling
3.5.4 Equipment Maintenance
3.6 Robotics and Automation in AI Agriculture
3.6.1 Robotic Planting System
3.6.2 Automated Irrigation Systems
3.6.3 AI-Driven Crop Monitoring
3.6.4 Harvesting Robots
3.7 Livestock Monitoring
3.7.1 Video and Image Analysis
3.7.2 Health Monitoring
3.7.3 Behavior Analysis
3.7.4 Predictive Analysis
3.7.5 Environment Analysis
3.7.6 Disease Analysis and Prediction
3.8 AI for Climate Smart Agriculture
3.8.1 Climate Prediction and Weather Forecasting
3.8.2 Enhancing Resilience to Climate Variability
3.8.3 Water Management
3.8.4 Reducing Greenhouse Gas Emissions
3.8.5 Increasing Productivity and Sustainability
3.9 AI in Agroecology
3.9.1 Decision Support Systems
3.9.2 Biodiversity Conservation
3.9.3 Soil Health Management
3.10 Soil Analysis
3.10.1 Soil Classification
3.10.2 Soil Nutrient Management
3.10.3 Disease and Pest Detection
3.10.4 Soil Moisture Monitoring
3.10.5 Precision Agriculture
3.10.6 Soil Erosion Prediction
3.10.7 Soil Remediation
3.11 Conclusion
Bibliography
4. Integrating Artificial Intelligence into Sustainable Agriculture: Advancements, Challenges, and Applications
Djamel Saba and Abdelkader Hadidi
4.1 Introduction
4.2 Literature Review
4.3 Key Critical Challenges of Conventional Agriculture
4.3.1 Overview of Conventional Agriculture
4.3.2 The Distinction Between Agriculture in the Past and Now
4.4 AI Technologies and Sustainable Agriculture
4.5 Artificial Intelligence’s Practical Use in Farming
4.6 Challenges and Ethical Considerations
4.6.1 Challenges
4.6.1.1 Data Privacy and Security
4.6.1.2 Accessibility and Inclusivity
4.6.1.3 Algorithm Bias
4.6.1.4 Interoperability and Standardization
4.6.1.5 Job Displacement
4.6.2 Ethical Considerations
4.6.2.1 Transparency and Accountability
4.6.2.2 Environmental Impact
4.6.2.3 Informed Consent
4.6.2.4 Fair Distribution of Benefits
4.6.2.5 Long-Term Sustainability
4.7 Conclusions and Further Work
References
5. Artificial Intelligence for Sustainable and Smart Agriculture
Djamel Saba and Abdelkader Hadidi
5.1 Introduction
5.2 Literature Review
5.3 AI Techniques for Revolutionizing Traditional Farming
5.4 Role of the IoT in Smart Farms
5.4.1 Smart Farming Technologies
5.4.1.1 Precision Agriculture
5.4.1.2 Livestock Monitoring
5.4.1.3 Crop Monitoring
5.4.2 Climate Management and Weather Forecasting
5.4.3 Supply Chain Optimization
5.4.4 Analytics and Assistance for Decision-Making
5.4.5 The Advantages and Difficulties of IoT in Agriculture
5.4.5.1 Advantages
5.4.5.2 Difficulties
5.5 Environmental Concerns Related to Agriculture
5.5.1 Environmental Concerns Related to Sustainable Agriculture
5.5.2 Environmental Concerns Related to Smart Agriculture
5.6 Challenges and Considerations
5.7 Conclusions and Further Work
References
6. Data-Driven Approaches for Sustainable Agriculture and Food Security
S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun
6.1 Introduction
6.1.1 The Role of Data in Agriculture
6.1.2 Importance of Sustainability and Food Security
6.1.3 Overview of Data-Driven Technologies
6.2 Big Data in Agriculture
6.2.1 Definition and Characteristics of Big Data
6.2.2 Applications of Big Data in Agriculture
6.2.3 Challenges and Opportunities
6.2.3.1 Challenges
6.2.3.2 Opportunities
6.3 Internet of Things (IoT) in Agriculture
6.3.1 Understanding IoT and Its Components
6.3.2 IoT Applications in Farming
6.3.3 Benefits and Challenges of IoT Implementation
6.4 Artificial Intelligence and Machine Learning in Agriculture
6.4.1 Fundamentals of AI and Machine Learning
6.4.2 AI and ML Applications in Crop Monitoring and Management
6.4.3 Predictive Analytics for Yield Optimization
6.5 Remote Sensing and GIS in Agriculture
6.5.1 Remote Sensing Technologies Overview
6.5.2 GIS Mapping for Precision Agriculture
6.5.3 Monitoring Environmental Impact and Land Use
6.6 Data-Driven Approaches for Sustainable Crop Management
6.6.1 Precision Agriculture Techniques
6.6.2 Crop Disease Detection and Management
6.6.3 Water Management and Irrigation Systems
6.7 Data-Driven Livestock Management
6.7.1 Monitoring Animal Health and Welfare
6.7.2 Precision Livestock Farming
6.7.3 Sustainable Feed Management
6.8 Supply Chain Management and Food Security
6.8.1 Traceability and Transparency in the Food Supply Chain
6.8.2 Data-Driven Approaches for Food Distribution
6.8.3 Enhancing Food Security through Data Analytics
6.9 Policy Implications and Ethical Considerations
6.9.1 Regulatory Frameworks for Data-Driven Agriculture
6.9.2 Ethical Issues Surrounding Data Collection and Privacy
6.9.3 Balancing Innovation with Social Responsibility
6.10 Future Trends and Conclusion
6.10.1 Emerging Technologies and Trends
6.10.2 Potential Impact on Sustainable Agriculture and Food Security
6.11 Conclusion
References
Part II: Recent Developments in Crop Disease Detection and Prevention
7. Advances in Plant Disease Detection and Classification Systems
Bhakti Sanket Puranik, Karanbir Singh Pelia, Shrivatsasingh Khushal Rathore and Vaibhav Vikas Dighe
7.1 Introduction
7.2 Literature Review
7.3 Methodologies and Techniques
7.3.1 CNN Architectures
7.3.2 Activation Functions
7.3.3 Loss Functions
7.3.4 Learning Rate Schedulers
7.3.5 Early Stopping
7.3.6 Checkpoints and Callbacks
7.3.7 Data Preprocessing
7.3.8 Data Augmentation
7.3.9 Transfer Learning
7.3.10 Ensemble Learning
7.4 Challenges and Limitations
7.4.1 Dataset Scarcity
7.4.2 Image Variability
7.4.3 Label Inconsistency
7.4.4 Model Interpretability
7.5 Proposed Model
7.5.1 Model Architecture
7.5.2 Training Mechanism
7.6 Future Scope
7.6.1 Development of Comprehensive Datasets
7.6.2 Exploration of Novel Architectures
7.6.3 Integration of Advanced Technologies
7.6.4 Crowdsourcing New Data
7.6.5 Adaptation and Interaction
7.6.6 Integrated Remediation Strategies
7.7 Conclusion
References
8. Ensemble-Based Crop Disease Biomarker Multi-Domain Feature Analysis (ECDBMFA)
Chilakalapudi Malathi and Sheela J.
8.1 Introduction
8.2 Literature Survey
8.3 Design of ECDBMFA
8.4 Result Evaluation and Comparative Analysis with Existing Techniques
8.5 Conclusion
References
9. Artificial Intelligence and Machine Learning in Crop Yield Prediction and Pest Control
Archana Negi, Jitendra Singh, Robin Kumar, Atin Kumar, Nisha and Sharad Sachan
Introduction
Artificial Intelligence
Machine Learning
AI-Based ML Algorithm Models
Some Important Evaluation Metrics Used in AI-Based Predictive Models
Applications of Artificial Intelligence and Machine Learning in Crop Yield Prediction Models
AI-Based Crop Yield Prediction Method—Case Study
Steps for Crop Yield Prediction
Applications of Artificial Intelligence and Machine Learning in Pest and Disease Management
Advantages of Using Artificial Intelligence/Machine Learning in Agriculture
Challenges of Artificial Intelligence and Machine Learning Application in Agriculture
Conclusion and Future Prospects
References
10. Farming in the Digital Age: A Machine Learning Enhanced Crop Yield Prediction and Recommendation System
Arti Sonawane, Akanksha Ranade, Apurva Kolte, Siddharth Daundkar and Shreyas Rajage
10.1 Background
10.2 Introduction
10.3 Importance
10.4 Machine Learning in Agriculture
10.5 Objectives
10.6 Related Work
10.6.1 Research Gaps
10.7 Proposed Methodology
10.7.1 Data Collection
10.7.2 Data Preprocessing
10.7.3 Training and Testing Model
10.7.4 Decision Tree Repressor
10.7.5 Random Forest Regressor
10.8 Implications for Farmers
10.9 Future Directions
10.10 Conclusion
References
Part III: IoT and Modern Agriculture
11. Digital Agriculture: IoT Applications and Technological Advancement
K. Aditya Shastry
11.1 Introduction
11.2 Related Work
11.3 Emerging Technologies and Related Applications in Smart Agriculture
11.3.1 Internet of Things (IoT) in Agriculture
11.3.2 Artificial Intelligence (AI) and Machine Learning (ML)
11.3.3 Remote Sensing (RS) and Satellite Technology
11.3.4 Blockchain Technology
11.3.5 Robotics and Automation
11.3.6 Sustainable Agriculture Practices
11.4 Challenges in Smart Farming
11.5 Future Trends in Smart Farming
11.6 Conclusion
References
12. IoT in Climate-Smart Farming
Maitreyi Darbha, S. V. Sanjay Kumar, S. R. Mani Sekhar and Sanjay H. A.
12.1 Introduction
12.2 IoT in Agriculture
12.2.1 What is IoT?
12.2.2 Methods Involved in the Incorporation of IoT in Agriculture
12.2.2.1 Greenhouse Farming
12.2.2.2 Vertical Farming
12.2.2.3 Hydroponics
12.2.2.4 Phenotyping
12.2.3 Resources Required for the Incorporation
12.3 Climate-Smart Farming Practices
12.3.1 What is Climate-Smart Farming?
12.3.2 Integration of IoT
12.3.2.1 Precision Farming
12.3.2.2 Smart Irrigation
12.3.2.3 Crop Monitoring
12.3.2.4 Livestock Management
12.3.3 Environmental Impact and Resilience to Climate Change
12.4 Case Studies
12.4.1 IoT Applications in Precision Agriculture
12.4.1.1 Weather Monitoring
12.4.1.2 Soil Content Monitoring
12.4.1.3 Diseases Monitoring
12.4.2 IoT Applications in Greenhouse
12.5 Evaluation of IoT Technologies
12.5.1 Effectiveness of IoT Technologies
12.5.2 Comparison with Traditional Methods
12.5.3 Advantages and Disadvantages
12.6 Relevance to Current-Day Global Issues
12.6.1 Future Scope
12.7 Conclusion
References
Part IV: Technological Trends and Advancements in the Agricultural Sector
13. Sustainable Agriculture Practices with ICT for Soil Health Management
Bhabani Prasad Mondal, Anshuman Kohli, Ingle Sagar Nandulal, Roheet Bhatnagar,
Chandan Kumar Panda, Sonal Kumari, Bharat Lal, Sai Parasar Das, Chandrabhan Patel, Vimal Kumar, Achin Kumar, Karad Gaurav Uttamrao, Suman Dutta and Ali R.A. Moursy
13.1 Introduction
13.2 Advanced ICT Technologies
13.2.1 GPS
13.2.2 GIS
13.2.3 DSS
13.2.4 Remote Sensing
13.2.5 IoT
13.2.6 Sensor Technology
13.2.7 Grid Soil Sampling and Variable Rate Technology (VRT)
13.2.8 Agricultural Robotics
13.3 Application of ICT in Soil Health Management
13.3.1 Artificial Intelligence in Analyzing Soil Health Parameters
13.3.1.1 Data Collection
13.3.1.2 Data Preprocessing
13.3.1.3 Feature Selection
13.3.1.4 Model Training
13.3.1.5 Model Validation
13.3.1.6 Soil Health Parameter Prediction
13.3.2 Fertilizer Recommendation Using ICT
13.3.2.1 Soil App
13.3.2.2 Multimodal DSS in Soil Fertility Management
13.3.3 Smart Soil Health Management Using Sensor-Based Technology
13.3.3.1 Sensor Selection
13.3.3.2 Sensor Placement
13.3.3.3 Data Collection
13.3.3.4 Data Processing
13.3.4 Real-Time Monitoring
13.3.4.1 Sensors’ Efficiency Evaluation
13.3.5 Satellite and Drone-Based Remote Sensing Technology in Soil Health Management
13.3.6 ICT-Based Soil Conservation for Soil Health Management
13.3.7 Autonomous Robots in Efficient Soil Health Management
13.4 Challenges in Implementing ICT-Based Technologies
13.4.1 Lack of Availability of Accurate Data
13.4.2 High Cost of Technology and Higher Investment
13.4.3 Lack of Sound Skill and Knowledge of Farmers
13.4.4 Lack of Communication Structure and Support
13.4.5 Low-Risk–Bearing Capacity of Farmers
13.5 Opportunities or Pathways to Tackle the Issues in ICT-Based Soil Management
13.6 Conclusion
Acknowledgment
References
14. Water Resource Management Model for Smart Agriculture
Aysulu Aydarova
Introduction
Main Part
Conclusion
References
15. A Big Data Analytics–Based Architecture for Smart Farming
Tanvi Chawla, Tamanna Gahlawat and TanyaShree Thakur
15.1 Introduction
15.2 Related Work
15.3 Research Issues in Big Data for Smart Agriculture
15.4 Applications of Big Data Analytics in Smart Agriculture
15.5 Types of Big Data in Agriculture
15.6 Proposed Work
15.7 Conclusion and Future Work
References
16. Adoption of Blockchain Technology for Transparent and Secure Agricultural Transactions
S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun
16.1 Introduction to Blockchain Technology
16.1.1 Definition and Overview
16.1.2 Evolution of Blockchain
16.1.3 Basic Components and Principles
16.1.4 Blockchain’s Significance in Agriculture
16.2 Challenges in Traditional Agricultural Transactions
16.2.1 Lack of Transparency
16.2.2 Security Issues
16.2.3 Trust Deficit
16.2.4 Inefficiencies in Supply Chain
16.3 Understanding Blockchain Solutions
16.3.1 How Blockchain Operates
16.3.2 Types of Blockchain
16.3.3 Smart Contracts and Their Role
16.3.4 Benefits of Blockchain in Agriculture
16.4 Use Cases of Blockchain in Agriculture
16.4.1 Produce Traceability
16.4.1.1 Tracking Farm to Fork
16.4.1.2 Quality Assurance
16.4.2 Supply Chain Management
16.4.2.1 Inventory Tracking
16.4.2.2 Real-Time Monitoring
16.4.3 Payment and Financing Solutions
16.4.3.1 Microfinancing for Farmers
16.4.3.2 Instant and Secure Payments
16.5 Implementing Blockchain in Agriculture
16.5.1 Infrastructure Requirements
16.5.2 Data Management and Integration
16.5.3 Regulatory Considerations
16.5.4 Challenges in Adoption
16.6 Case Studies and Success Stories
16.6.1 IBM Food Trust
16.6.2 Provenance
16.6.3 AgriDigital
16.7 Future Trends and Opportunities
16.7.1 Integration with IoT and AI
16.7.2 Expansion of Blockchain Applications
16.7.3 Potential Impact on Global Food Security
16.8 Conclusion
References
17. AI-Assisted Environmental Parameter Monitoring of Plants in Greenhouse Farming
K. Sujatha, N.P.G. Bhavani, R. S. Ponmagal, N. Shanmugasundaram, C. Tamilselvi, A. Ganesan and Suqun Cao
17.1 Introduction
17.2 Background
17.3 Importance of Smart Agriculture
17.4 Artificial Neural Network (ANN)
17.4.1 Mayfly Optimization
17.5 Problem Statement
17.6 Objectives
17.7 Strategy for Polyhouse Monitoring
17.8 Results and Discussion
17.9 Conclusion
References
18. Metaverse in Agricultural Training and Simulation
Syed Quadir Moinuddin, Himam Saheb Shaik, Md Atiqur Rahman and Borigorla Venu
18.1 Introduction
18.2 AI in Agriculture
18.3 Metaverse
18.3.1 Agriculture with AI-Based Metaverse
18.4 Augmented Reality (AR)
18.5 Virtual Reality (VR)
18.6 Mixed Reality (MR)
18.7 Agriculture Training Simulations
18.8 Metaverse in Agriculture Trainings
18.9 Conclusions
Acknowledgment
References
19. Sustainable Farming in the Digital Era: AI and IoT Technologies Transforming Agriculture
Arti Sonawane, Suvarna Patil and Atul Kathole
19.1 Introduction
19.1.1 The Role of Artificial Intelligence in Agriculture
19.1.2 The Role of the Internet of Things in Agriculture
19.1.3 The Intersection of AI and IoT in Agriculture
19.1.4 The Importance of Sustainability in Agriculture
19.1.5 Problem Statement
19.1.6 Motivation
19.1.7 Objective
19.2 Related Work
19.2.1 Comparative Analysis of Existing Challenges
19.2.1.1 Precision Agriculture: Challenges in Future IoT (2023)
19.2.1.2 AI-Driven Precision Agriculture: Challenges and Perspectives (2023)
19.2.1.3 IoT and AI in Agriculture: An Overview (2022)
19.2.1.4 Smart Farming with IoT and AI: Benefits and Challenges (2022)
19.2.1.5 AI and IoT-Based Crop Monitoring: A Review (2023)
19.2.1.6 Integration of AI and IoT in Agriculture: State-of-the-Art and Future Trends (2023)
19.2.1.7 Sustainable Agriculture: The Role of IoT and AI (2022)
19.2.1.8 Advances in IoT and AI for Precision Agriculture (2022)
19.3 Discussion of Proposed Approach
19.3.1 System Architecture
19.3.2 Components and Tools
19.3.3 Result and Discussion
19.4 Application
19.5 Advantages and Disadvantages of System
19.6 Conclusion
Future Scope
References
20. Precision Agriculture with Unmanned Aerial Vehicles
Suresh S., Sampath Boopathi, Elayaraja R., Velmurugan D. and Selvapriya R.
20.1 Introduction
20.2 Agri-UAV Construction and Controls
20.3 Applications of UAVs in Agriculture
20.3.1 Crop Spraying
20.3.2 Crop Health Monitoring
20.3.3 Drone Seeding
20.4 Conclusion
References
Index

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