Optimizing AI Applications for Sustainable Agriculture

Inbunden, Engelska, 2025

AvRoheet Bhatnagar,Chandan Kumar Panda,Mahmoud Yasin Shams,India) Bhatnagar, Roheet (Manipal University Jaipur, Jaipur, Rajasthan,India) Panda, Chandan Kumar (Bihar Agricultural University, Sabour, Bhagalpur, Bihar,Egypt) Shams, Mahmoud Yasin (Kafrelsheikh University, Kafr el-Sheikh

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

Produktinformation

Utgivningsdatum2025-11-03
FormatInbunden
SpråkEngelska
Antal sidor576
FörlagJohn Wiley & Sons Inc
ISBN9781394287239

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Artificiell intelligens inom Data och IT

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.

Preface xxiPart I: Artificial Intelligence-Assisted Sustainable Agriculture 11 AI and Emerging Technologies for Precision Agriculture: A Survey 3Brajesh Kumar Khare1.1 Introduction 41.2 Precision Agriculture 51.3 Artificial Intelligence 91.3.1 Role of AI in Agriculture 111.4 Internet of Things (IoT) 111.4.1 Basics of IoT in Agriculture 131.4.2 Role of IoT 151.5 Blockchain Technology 151.6 Technologies Used in Smart Farming 171.6.1 Global Positioning System (GPS) 171.6.2 Sensor Technologies 171.6.3 Variable Rate Technology and Grid Soil Sampling 181.6.4 Geographic Information System (GIS) 191.6.5 Crop Management 191.6.6 Soil and Plant Sensors 201.6.7 Yield Monitor 201.7 Challenges 241.8 Future Research 261.9 Conclusion 29References 292 AI-Enabled Framework for Sustainable Agriculture Practices 33Yukti Batra, Suman Bhatia and Ankit Verma2.1 Introduction 342.2 Sustainable Agriculture Imperatives 352.2.1 Environmental Degradation 362.2.2 Biodiversity Loss 362.2.3 Climate Change Impacts 362.2.4 Resource Scarcity 372.2.5 Food Security and Economic Stability 372.2.6 Public Health Concerns 372.2.7 Social Equity and Rural Livelihoods 372.2.8 Global Food Shortage Concerns 382.2.9 Empowerment and Awareness 382.3 Social Relevance of Sustainable Practices in Agriculture 382.3.1 Livelihood Security 392.3.2 Community Health and Well-Being 392.3.3 Social Equity and Inclusion 392.3.4 Rural Empowerment and Resilience 402.4 Sustainable Agriculture Indicators 402.4.1 Food Grain Productivity 402.4.2 Population Density 412.4.3 Cropping Intensity 422.5 Sustainable Agriculture Practices Followed Till Date 422.5.1 Agroforestry 422.5.2 Integrated Pest Management (IPM) 442.5.3 Crop Rotation 442.5.4 Cover Cropping 442.5.5 Organic Farming 442.5.6 No-Till Farming 442.6 AI-Enabled Conceptual Framework 442.6.1 Perception from Environment Using IoT Sensors 452.6.1.1 Remote Sensing 452.6.1.2 IoT Sensors 462.6.2 Data Storage 462.6.3 Data Processing 472.6.4 Training and Testing by ML Models 472.7 Applications of Artificial Intelligence in Agriculture 482.8 Challenges and Barriers to Sustainable Agriculture 512.8.1 Theoretical Obstacles 512.8.2 Methodological Obstacles 522.8.3 Personal Obstacles 532.8.4 Practical Obstacles 542.9 Future Directions 552.10 Conclusion 57References 583 The Impact of Artificial Intelligence on Agriculture: Revolutionizing Efficiency and Sustainability 61Santhiya S., P. Jayadharshini, N. Abinaya, Sharmila C., Srigha S. and Sruthi K.Applications 623.1 Introduction 623.2 Precision Farming 643.2.1 Data Collection and Analytics 643.2.2 Disease Detection 653.2.3 Yield Production and Optimization 653.2.4 Precision Irrigation 663.3 Crop Monitoring 673.3.1 Remote Sensing and Satellite Imagery 673.3.2 Drones 673.3.3 Computer Vision and Image Analysis 683.3.4 Sensor Network and IoT 683.3.5 Weed Detection Management 683.4 AI in Aquaculture 693.4.1 Monitoring Water Quality 693.4.2 Feed Management 703.4.3 Breeding Technique 703.4.4 Autonomous Systems and Market Optimization 703.5 Predictive Analysis 713.5.1 Irrigation Optimization 713.5.2 Supply Chain Management 723.5.3 Weather and Climate Modeling 723.5.4 Equipment Maintenance 733.6 Robotics and Automation in AI Agriculture 733.6.1 Robotic Planting System 733.6.2 Automated Irrigation Systems 743.6.3 AI-Driven Crop Monitoring 753.6.4 Harvesting Robots 753.7 Livestock Monitoring 753.7.1 Video and Image Analysis 763.7.2 Health Monitoring 763.7.3 Behavior Analysis 773.7.4 Predictive Analysis 773.7.5 Environment Analysis 773.7.6 Disease Analysis and Prediction 783.8 AI for Climate Smart Agriculture 783.8.1 Climate Prediction and Weather Forecasting 793.8.2 Enhancing Resilience to Climate Variability 793.8.3 Water Management 803.8.4 Reducing Greenhouse Gas Emissions 803.8.5 Increasing Productivity and Sustainability 803.9 AI in Agroecology 813.9.1 Decision Support Systems 813.9.2 Biodiversity Conservation 823.9.3 Soil Health Management 823.10 Soil Analysis 833.10.1 Soil Classification 833.10.2 Soil Nutrient Management 833.10.3 Disease and Pest Detection 843.10.4 Soil Moisture Monitoring 843.10.5 Precision Agriculture 843.10.6 Soil Erosion Prediction 853.10.7 Soil Remediation 853.11 Conclusion 86Bibliography 874 Integrating Artificial Intelligence into Sustainable Agriculture: Advancements, Challenges, and Applications 89Djamel Saba and Abdelkader Hadidi4.1 Introduction 904.2 Literature Review 924.3 Key Critical Challenges of Conventional Agriculture 974.3.1 Overview of Conventional Agriculture 974.3.2 The Distinction Between Agriculture in the Past and Now 994.4 AI Technologies and Sustainable Agriculture 1034.5 Artificial Intelligence’s Practical Use in Farming 1044.6 Challenges and Ethical Considerations 1074.6.1 Challenges 1074.6.1.1 Data Privacy and Security 1074.6.1.2 Accessibility and Inclusivity 1074.6.1.3 Algorithm Bias 1074.6.1.4 Interoperability and Standardization 1074.6.1.5 Job Displacement 1084.6.2 Ethical Considerations 1084.6.2.1 Transparency and Accountability 1084.6.2.2 Environmental Impact 1084.6.2.3 Informed Consent 1084.6.2.4 Fair Distribution of Benefits 1094.6.2.5 Long-Term Sustainability 1094.7 Conclusions and Further Work 109References 1105 Artificial Intelligence for Sustainable and Smart Agriculture 117Djamel Saba and Abdelkader Hadidi5.1 Introduction 1185.2 Literature Review 1205.3 AI Techniques for Revolutionizing Traditional Farming 1255.4 Role of the IoT in Smart Farms 1285.4.1 Smart Farming Technologies 1305.4.1.1 Precision Agriculture 1305.4.1.2 Livestock Monitoring 1305.4.1.3 Crop Monitoring 1305.4.2 Climate Management and Weather Forecasting 1305.4.3 Supply Chain Optimization 1315.4.4 Analytics and Assistance for Decision-Making 1315.4.5 The Advantages and Difficulties of IoT in Agriculture 1315.4.5.1 Advantages 1315.4.5.2 Difficulties 1315.5 Environmental Concerns Related to Agriculture 1325.5.1 Environmental Concerns Related to Sustainable Agriculture 1325.5.2 Environmental Concerns Related to Smart Agriculture 1325.6 Challenges and Considerations 1355.7 Conclusions and Further Work 137References 1426 Data-Driven Approaches for Sustainable Agriculture and Food Security 145S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun6.1 Introduction 1466.1.1 The Role of Data in Agriculture 1466.1.2 Importance of Sustainability and Food Security 1476.1.3 Overview of Data-Driven Technologies 1486.2 Big Data in Agriculture 1506.2.1 Definition and Characteristics of Big Data 1506.2.2 Applications of Big Data in Agriculture 1516.2.3 Challenges and Opportunities 1526.2.3.1 Challenges 1526.2.3.2 Opportunities 1536.3 Internet of Things (IoT) in Agriculture 1546.3.1 Understanding IoT and Its Components 1546.3.2 IoT Applications in Farming 1556.3.3 Benefits and Challenges of IoT Implementation 1566.4 Artificial Intelligence and Machine Learning in Agriculture 1576.4.1 Fundamentals of AI and Machine Learning 1576.4.2 AI and ML Applications in Crop Monitoring and Management 1586.4.3 Predictive Analytics for Yield Optimization 1596.5 Remote Sensing and GIS in Agriculture 1596.5.1 Remote Sensing Technologies Overview 1596.5.2 GIS Mapping for Precision Agriculture 1606.5.3 Monitoring Environmental Impact and Land Use 1616.6 Data-Driven Approaches for Sustainable Crop Management 1626.6.1 Precision Agriculture Techniques 1626.6.2 Crop Disease Detection and Management 1626.6.3 Water Management and Irrigation Systems 1636.7 Data-Driven Livestock Management 1636.7.1 Monitoring Animal Health and Welfare 1636.7.2 Precision Livestock Farming 1646.7.3 Sustainable Feed Management 1646.8 Supply Chain Management and Food Security 1656.8.1 Traceability and Transparency in the Food Supply Chain 1656.8.2 Data-Driven Approaches for Food Distribution 1656.8.3 Enhancing Food Security through Data Analytics 1666.9 Policy Implications and Ethical Considerations 1676.9.1 Regulatory Frameworks for Data-Driven Agriculture 1676.9.2 Ethical Issues Surrounding Data Collection and Privacy 1676.9.3 Balancing Innovation with Social Responsibility 1686.10 Future Trends and Conclusion 1686.10.1 Emerging Technologies and Trends 1686.10.2 Potential Impact on Sustainable Agriculture and Food Security 1696.11 Conclusion 170References 170Part II: Recent Developments in Crop Disease Detection and Prevention 1757 Advances in Plant Disease Detection and Classification Systems 177Bhakti Sanket Puranik, Karanbir Singh Pelia, Shrivatsasingh Khushal Rathore and Vaibhav Vikas Dighe7.1 Introduction 1787.2 Literature Review 1797.3 Methodologies and Techniques 1857.3.1 CNN Architectures 1857.3.2 Activation Functions 1867.3.3 Loss Functions 1877.3.4 Learning Rate Schedulers 1877.3.5 Early Stopping 1887.3.6 Checkpoints and Callbacks 1887.3.7 Data Preprocessing 1897.3.8 Data Augmentation 1897.3.9 Transfer Learning 1907.3.10 Ensemble Learning 1917.4 Challenges and Limitations 1917.4.1 Dataset Scarcity 1927.4.2 Image Variability 1927.4.3 Label Inconsistency 1937.4.4 Model Interpretability 1937.5 Proposed Model 1947.5.1 Model Architecture 1957.5.2 Training Mechanism 1967.6 Future Scope 1987.6.1 Development of Comprehensive Datasets 1997.6.2 Exploration of Novel Architectures 1997.6.3 Integration of Advanced Technologies 2007.6.4 Crowdsourcing New Data 2017.6.5 Adaptation and Interaction 2017.6.6 Integrated Remediation Strategies 2027.7 Conclusion 203References 2048 Ensemble-Based Crop Disease Biomarker Multi-Domain Feature Analysis (ECDBMFA) 207Chilakalapudi Malathi and Sheela J.8.1 Introduction 2088.2 Literature Survey 2088.3 Design of ECDBMFA 2108.4 Result Evaluation and Comparative Analysis with Existing Techniques 2178.5 Conclusion 226References 2269 Artificial Intelligence and Machine Learning in Crop Yield Prediction and Pest Control 231Archana Negi, Jitendra Singh, Robin Kumar, Atin Kumar, Nisha and Sharad SachanIntroduction 232Artificial Intelligence 234Machine Learning 235AI-Based ML Algorithm Models 237Some Important Evaluation Metrics Used in AI-Based Predictive Models 239Applications of Artificial Intelligence and Machine Learning in Crop Yield Prediction Models 241AI-Based Crop Yield Prediction Method—Case Study 242Steps for Crop Yield Prediction 243Applications of Artificial Intelligence and Machine Learning in Pest and Disease Management 244Advantages of Using Artificial Intelligence/Machine Learning in Agriculture 248Challenges of Artificial Intelligence and Machine Learning Application in Agriculture 249Conclusion and Future Prospects 250References 25010 Farming in the Digital Age: A Machine Learning Enhanced Crop Yield Prediction and Recommendation System 257Arti Sonawane, Akanksha Ranade, Apurva Kolte, Siddharth Daundkar and Shreyas Rajage10.1 Background 25810.2 Introduction 26010.3 Importance 26110.4 Machine Learning in Agriculture 26210.5 Objectives 26710.6 Related Work 26710.6.1 Research Gaps 27610.7 Proposed Methodology 27710.7.1 Data Collection 27710.7.2 Data Preprocessing 27710.7.3 Training and Testing Model 27810.7.4 Decision Tree Repressor 27810.7.5 Random Forest Regressor 27910.8 Implications for Farmers 28210.9 Future Directions 28410.10 Conclusion 285References 285Part III: IoT and Modern Agriculture 28911 Digital Agriculture: IoT Applications and Technological Advancement 291K. Aditya Shastry11.1 Introduction 29211.2 Related Work 29611.3 Emerging Technologies and Related Applications in Smart Agriculture 29911.3.1 Internet of Things (IoT) in Agriculture 30011.3.2 Artificial Intelligence (AI) and Machine Learning (ml) 30011.3.3 Remote Sensing (RS) and Satellite Technology 30211.3.4 Blockchain Technology 30511.3.5 Robotics and Automation 30911.3.6 Sustainable Agriculture Practices 31011.4 Challenges in Smart Farming 31511.5 Future Trends in Smart Farming 31711.6 Conclusion 320References 32012 IoT in Climate-Smart Farming 323Maitreyi Darbha, S. V. Sanjay Kumar, S. R. Mani Sekhar and Sanjay H. A.12.1 Introduction 32312.2 IoT in Agriculture 32512.2.1 What is IoT? 32512.2.2 Methods Involved in the Incorporation of IoT in Agriculture 32512.2.2.1 Greenhouse Farming 32512.2.2.2 Vertical Farming 32612.2.2.3 Hydroponics 32612.2.2.4 Phenotyping 32712.2.3 Resources Required for the Incorporation 32812.3 Climate-Smart Farming Practices 32912.3.1 What is Climate-Smart Farming? 32912.3.2 Integration of IoT 33012.3.2.1 Precision Farming 33012.3.2.2 Smart Irrigation 33112.3.2.3 Crop Monitoring 33112.3.2.4 Livestock Management 33112.3.3 Environmental Impact and Resilience to Climate Change 33212.4 Case Studies 33312.4.1 IoT Applications in Precision Agriculture 33312.4.1.1 Weather Monitoring 33312.4.1.2 Soil Content Monitoring 33312.4.1.3 Diseases Monitoring 33412.4.2 IoT Applications in Greenhouse 33412.5 Evaluation of IoT Technologies 33612.5.1 Effectiveness of IoT Technologies 33612.5.2 Comparison with Traditional Methods 33612.5.3 Advantages and Disadvantages 33712.6 Relevance to Current-Day Global Issues 33812.6.1 Future Scope 33812.7 Conclusion 339References 340Part IV: Technological Trends and Advancements in the Agricultural Sector 34513 Sustainable Agriculture Practices with ICT for Soil Health Management 347Bhabani 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. Moursy13.1 Introduction 34813.2 Advanced ICT Technologies 35013.2.1 Gps 35013.2.2 Gis 35113.2.3 Dss 35213.2.4 Remote Sensing 35213.2.5 IoT 35313.2.6 Sensor Technology 35413.2.7 Grid Soil Sampling and Variable Rate Technology (vrt) 35613.2.8 Agricultural Robotics 35713.3 Application of ICT in Soil Health Management 35813.3.1 Artificial Intelligence in Analyzing Soil Health Parameters 35813.3.1.1 Data Collection 35813.3.1.2 Data Preprocessing 35813.3.1.3 Feature Selection 35813.3.1.4 Model Training 35913.3.1.5 Model Validation 35913.3.1.6 Soil Health Parameter Prediction 35913.3.2 Fertilizer Recommendation Using ICT 35913.3.2.1 Soil App 36013.3.2.2 Multimodal DSS in Soil Fertility Management 36013.3.3 Smart Soil Health Management Using Sensor-Based Technology 36213.3.3.1 Sensor Selection 36213.3.3.2 Sensor Placement 36213.3.3.3 Data Collection 36213.3.3.4 Data Processing 36213.3.4 Real-Time Monitoring 36313.3.4.1 Sensors’ Efficiency Evaluation 36313.3.5 Satellite and Drone-Based Remote Sensing Technology in Soil Health Management 36313.3.6 ICT-Based Soil Conservation for Soil Health Management 36413.3.7 Autonomous Robots in Efficient Soil Health Management 36513.4 Challenges in Implementing ICT-Based Technologies 36513.4.1 Lack of Availability of Accurate Data 36513.4.2 High Cost of Technology and Higher Investment 36613.4.3 Lack of Sound Skill and Knowledge of Farmers 36613.4.4 Lack of Communication Structure and Support 36713.4.5 Low-Risk–Bearing Capacity of Farmers 36713.5 Opportunities or Pathways to Tackle the Issues in ICT-Based Soil Management 36713.6 Conclusion 369Acknowledgment 370References 37014 Water Resource Management Model for Smart Agriculture 375Aysulu AydarovaIntroduction 375Main Part 376Conclusion 397References 39815 A Big Data Analytics–Based Architecture for Smart Farming 399Tanvi Chawla, Tamanna Gahlawat and TanyaShree Thakur15.1 Introduction 40015.2 Related Work 40215.3 Research Issues in Big Data for Smart Agriculture 40415.4 Applications of Big Data Analytics in Smart Agriculture 40515.5 Types of Big Data in Agriculture 40715.6 Proposed Work 40815.7 Conclusion and Future Work 414References 41416 Adoption of Blockchain Technology for Transparent and Secure Agricultural Transactions 417S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun16.1 Introduction to Blockchain Technology 41816.1.1 Definition and Overview 41816.1.2 Evolution of Blockchain 41816.1.3 Basic Components and Principles 41916.1.4 Blockchain’s Significance in Agriculture 41916.2 Challenges in Traditional Agricultural Transactions 42016.2.1 Lack of Transparency 42016.2.2 Security Issues 42016.2.3 Trust Deficit 42116.2.4 Inefficiencies in Supply Chain 42116.3 Understanding Blockchain Solutions 42216.3.1 How Blockchain Operates 42216.3.2 Types of Blockchain 42316.3.3 Smart Contracts and Their Role 42416.3.4 Benefits of Blockchain in Agriculture 42516.4 Use Cases of Blockchain in Agriculture 42716.4.1 Produce Traceability 42716.4.1.1 Tracking Farm to Fork 42716.4.1.2 Quality Assurance 42716.4.2 Supply Chain Management 42816.4.2.1 Inventory Tracking 42816.4.2.2 Real-Time Monitoring 42816.4.3 Payment and Financing Solutions 42816.4.3.1 Microfinancing for Farmers 42816.4.3.2 Instant and Secure Payments 43016.5 Implementing Blockchain in Agriculture 43016.5.1 Infrastructure Requirements 43016.5.2 Data Management and Integration 43216.5.3 Regulatory Considerations 43216.5.4 Challenges in Adoption 43216.6 Case Studies and Success Stories 43416.6.1 IBM Food Trust 43416.6.2 Provenance 43416.6.3 AgriDigital 43416.7 Future Trends and Opportunities 43516.7.1 Integration with IoT and AI 43516.7.2 Expansion of Blockchain Applications 43516.7.3 Potential Impact on Global Food Security 43716.8 Conclusion 439References 43917 AI-Assisted Environmental Parameter Monitoring of Plants in Greenhouse Farming 445K. Sujatha, N.P.G. Bhavani, R. S. Ponmagal, N. Shanmugasundaram, C. Tamilselvi, A. Ganesan and Suqun Cao17.1 Introduction 44617.2 Background 44717.3 Importance of Smart Agriculture 44817.4 Artificial Neural Network (ANN) 44917.4.1 Mayfly Optimization 45117.5 Problem Statement 45317.6 Objectives 45417.7 Strategy for Polyhouse Monitoring 45417.8 Results and Discussion 46017.9 Conclusion 467References 46918 Metaverse in Agricultural Training and Simulation 471Syed Quadir Moinuddin, Himam Saheb Shaik, md Atiqur Rahman and Borigorla Venu18.1 Introduction 47118.2 AI in Agriculture 47318.3 Metaverse 47518.3.1 Agriculture with AI-Based Metaverse 47618.4 Augmented Reality (AR) 47818.5 Virtual Reality (VR) 48018.6 Mixed Reality (MR) 48218.7 Agriculture Training Simulations 48518.8 Metaverse in Agriculture Trainings 48718.9 Conclusions 488Acknowledgment 489References 48919 Sustainable Farming in the Digital Era: AI and IoT Technologies Transforming Agriculture 493Arti Sonawane, Suvarna Patil and Atul Kathole19.1 Introduction 49419.1.1 The Role of Artificial Intelligence in Agriculture 49519.1.2 The Role of the Internet of Things in Agriculture 49519.1.3 The Intersection of AI and IoT in Agriculture 49619.1.4 The Importance of Sustainability in Agriculture 49619.1.5 Problem Statement 49719.1.6 Motivation 49719.1.7 Objective 49719.2 Related Work 49819.2.1 Comparative Analysis of Existing Challenges 49919.2.1.1 Precision Agriculture: Challenges in Future IoT (2023) 50119.2.1.2 AI-Driven Precision Agriculture: Challenges and Perspectives (2023) 50219.2.1.3 IoT and AI in Agriculture: An Overview (2022) 50219.2.1.4 Smart Farming with IoT and AI: Benefits and Challenges (2022) 50219.2.1.5 AI and IoT-Based Crop Monitoring: A Review (2023) 50219.2.1.6 Integration of AI and IoT in Agriculture: State-of-the-Art and Future Trends (2023) 50219.2.1.7 Sustainable Agriculture: The Role of IoT and AI (2022) 50319.2.1.8 Advances in IoT and AI for Precision Agriculture (2022) 50319.3 Discussion of Proposed Approach 50319.3.1 System Architecture 50419.3.2 Components and Tools 50519.3.3 Result and Discussion 50619.4 Application 50819.5 Advantages and Disadvantages of System 50919.6 Conclusion 510Future Scope 510References 51120 Precision Agriculture with Unmanned Aerial Vehicles 513Suresh S., Sampath Boopathi, Elayaraja R., Velmurugan D. and Selvapriya R.20.1 Introduction 51420.2 Agri-UAV Construction and Controls 51620.3 Applications of UAVs in Agriculture 51920.3.1 Crop Spraying 52020.3.2 Crop Health Monitoring 52420.3.3 Drone Seeding 52720.4 Conclusion 529References 530Index 535