Overview

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.

Full Product Details

Author:   Roheet Bhatnagar (Manipal University Jaipur, Jaipur, Rajasthan, India) ,  Chandan Kumar Panda (Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India) ,  Mahmoud Yasin Shams (Kafrelsheikh University, Kafr el-Sheikh, Egypt)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
ISBN:  

9781394287239

ISBN 10:   1394287232
Pages:   576
Publication Date:   03 November 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

Preface xxi Part I: Artificial Intelligence-Assisted Sustainable Agriculture 1 1 AI and Emerging Technologies for Precision Agriculture: A Survey 3 Brajesh Kumar Khare 1.1 Introduction 4 1.2 Precision Agriculture 5 1.3 Artificial Intelligence 9 1.4 Internet of Things (IoT) 11 1.5 Blockchain Technology 15 1.6 Technologies Used in Smart Farming 17 1.7 Challenges 24 1.8 Future Research 26 1.9 Conclusion 29 2 AI-Enabled Framework for Sustainable Agriculture Practices 33 Yukti Batra, Suman Bhatia and Ankit Verma 2.1 Introduction 34 2.2 Sustainable Agriculture Imperatives 35 2.3 Social Relevance of Sustainable Practices in Agriculture 38 2.4 Sustainable Agriculture Indicators 40 2.5 Sustainable Agriculture Practices Followed Till Date 42 2.6 AI-Enabled Conceptual Framework 44 2.7 Applications of Artificial Intelligence in Agriculture 48 2.8 Challenges and Barriers to Sustainable Agriculture 51 2.9 Future Directions 55 2.10 Conclusion 57 3 The Impact of Artificial Intelligence on Agriculture: Revolutionizing Efficiency and Sustainability 61 Santhiya S., P. Jayadharshini, N. Abinaya, Sharmila C., Srigha S. and Sruthi K. 3.1 Introduction 62 3.2 Precision Farming 64 3.3 Crop Monitoring 67 3.4 AI in Aquaculture 69 3.5 Predictive Analysis 71 3.6 Robotics and Automation in AI Agriculture 73 3.7 Livestock Monitoring 75 3.8 AI for Climate Smart Agriculture 78 3.9 AI in Agroecology 81 3.10 Soil Analysis 83 3.11 Conclusion 86 4 Integrating Artificial Intelligence into Sustainable Agriculture: Advancements, Challenges, and Applications 89 Djamel Saba and Abdelkader Hadidi 4.1 Introduction 90 4.2 Literature Review 92 4.3 Key Critical Challenges of Conventional Agriculture 97 4.4 AI Technologies and Sustainable Agriculture 103 4.5 Artificial Intelligence’s Practical Use in Farming 104 4.6 Challenges and Ethical Considerations 107 4.7 Conclusions and Further Work 109 5 Artificial Intelligence for Sustainable and Smart Agriculture 117 Djamel Saba and Abdelkader Hadidi 5.1 Introduction 118 5.2 Literature Review 120 5.3 AI Techniques for Revolutionizing Traditional Farming 125 5.4 Role of the IoT in Smart Farms 128 5.5 Environmental Concerns Related to Agriculture 132 5.6 Challenges and Considerations 135 5.7 Conclusions and Further Work 137 6 Data-Driven Approaches for Sustainable Agriculture and Food Security 145 S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun 6.1 Introduction 146 6.2 Big Data in Agriculture 150 6.3 Internet of Things (IoT) in Agriculture 154 6.4 Artificial Intelligence and Machine Learning in Agriculture 157 6.5 Remote Sensing and GIS in Agriculture 159 6.6 Data-Driven Approaches for Sustainable Crop Management 162 6.7 Data-Driven Livestock Management 163 6.8 Supply Chain Management and Food Security 165 6.9 Policy Implications and Ethical Considerations 167 6.10 Future Trends and Conclusion 168 6.11 Conclusion 170 Part II: Recent Developments in Crop Disease Detection and Prevention 175 7 Advances in Plant Disease Detection and Classification Systems 177 Bhakti Sanket Puranik, Karanbir Singh Pelia, Shrivatsasingh Khushal Rathore and Vaibhav Vikas Dighe 7.1 Introduction 178 7.2 Literature Review 179 7.3 Methodologies and Techniques 185 7.4 Challenges and Limitations 191 7.5 Proposed Model 194 7.6 Future Scope 198 7.7 Conclusion 203 8 Ensemble-Based Crop Disease Biomarker Multi-Domain Feature Analysis (ECDBMFA) 207 Chilakalapudi Malathi and Sheela J. 8.1 Introduction 208 8.2 Literature Survey 208 8.3 Design of ECDBMFA 210 8.4 Result Evaluation and Comparative Analysis with Existing Techniques 217 8.5 Conclusion 226 9 Artificial Intelligence and Machine Learning in Crop Yield Prediction and Pest Control 231 Archana Negi, Jitendra Singh, Robin Kumar, Atin Kumar, Nisha and Sharad Sachan 9.1 Introduction 232 9.2 Artificial Intelligence 234 9.3 Machine Learning 235 9.4 AI-Based ML Algorithm Models 237 9.5 Some Important Evaluation Metrics Used in AI-Based Predictive Models 239 9.6 Applications of Artificial Intelligence and Machine Learning in Crop Yield Prediction Models 241 9.7 AI-Based Crop Yield Prediction Method—Case Study 242 9.8 Steps for Crop Yield Prediction 243 9.9 Applications of Artificial Intelligence and Machine Learning in Pest and Disease Management 244 9.10 Advantages of Using Artificial Intelligence/Machine Learning in Agriculture 248 9.11 Challenges of Artificial Intelligence and Machine Learning Application in Agriculture 249 9.12 Conclusion and Future Prospects 250 10 Farming in the Digital Age: A Machine Learning Enhanced Crop Yield Prediction and Recommendation System 257 Arti Sonawane, Akanksha Ranade, Apurva Kolte, Siddharth Daundkar and Shreyas Rajage 10.1 Background 258 10.2 Introduction 260 10.3 Importance 261 10.4 Machine Learning in Agriculture 262 10.5 Objectives 267 10.6 Related Work 267 10.7 Proposed Methodology 277 10.8 Implications for Farmers 282 10.9 Future Directions 284 10.10 Conclusion 285 Part III: IoT and Modern Agriculture 289 11 Digital Agriculture: IoT Applications and Technological Advancement 291 K. Aditya Shastry 11.1 Introduction 292 11.2 Related Work 296 11.3 Emerging Technologies and Related Applications in Smart Agriculture 299 11.4 Challenges in Smart Farming 315 11.5 Future Trends in Smart Farming 317 11.6 Conclusion 320 12 IoT in Climate-Smart Farming 323 Maitreyi Darbha, S. V. Sanjay Kumar, S. R. Mani Sekhar and Sanjay H. A. 12.1 Introduction 323 12.2 IoT in Agriculture 325 12.3 Climate-Smart Farming Practices 329 12.4 Case Studies 333 12.5 Evaluation of IoT Technologies 336 12.6 Relevance to Current-Day Global Issues 338 12.7 Conclusion 339 Part IV: Technological Trends and Advancements in the Agricultural Sector 345 13 Sustainable Agriculture Practices with ICT for Soil Health Management 347 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 348 13.2 Advanced ICT Technologies 350 13.3 Application of ICT in Soil Health Management 358 13.4 Challenges in Implementing ICT-Based Technologies 365 13.5 Opportunities or Pathways to Tackle the Issues in ICT-Based Soil Management 367 13.6 Conclusion 369 14 Water Resource Management Model for Smart Agriculture 375 Aysulu Aydarova 15 A Big Data Analytics–Based Architecture for Smart Farming 399 Tanvi Chawla, Tamanna Gahlawat and TanyaShree Thakur 15.1 Introduction 400 15.2 Related Work 402 15.3 Research Issues in Big Data for Smart Agriculture 404 15.4 Applications of Big Data Analytics in Smart Agriculture 405 15.5 Types of Big Data in Agriculture 407 15.6 Proposed Work 408 15.7 Conclusion and Future Work 414 16 Adoption of Blockchain Technology for Transparent and Secure Agricultural Transactions 417 S.C. Vetrivel, V. Sabareeshwari, K.C. Sowmiya and V.P. Arun 16.1 Introduction to Blockchain Technology 418 16.2 Challenges in Traditional Agricultural Transactions 420 16.3 Understanding Blockchain Solutions 422 16.4 Use Cases of Blockchain in Agriculture 427 16.5 Implementing Blockchain in Agriculture 430 16.6 Case Studies and Success Stories 434 16.7 Future Trends and Opportunities 435 6.8 Conclusion 439 17 AI-Assisted Environmental Parameter Monitoring of Plants in Greenhouse Farming 445 K. Sujatha, N.P.G. Bhavani, R. S. Ponmagal, N. Shanmugasundaram, C. Tamilselvi, A. Ganesan and Suqun Cao 17.1 Introduction 446 17.2 Background 447 17.3 Importance of Smart Agriculture 448 17.4 Artificial Neural Network (ANN) 449 17.5 Problem Statement 453 17.6 Objectives 454 17.7 Strategy for Polyhouse Monitoring 454 17.8 Results and Discussion 460 17.9 Conclusion 467 18 Metaverse in Agricultural Training and Simulation 471 Syed Quadir Moinuddin, Himam Saheb Shaik, Md Atiqur Rahman and Borigorla Venu 18.1 Introduction 471 18.2 AI in Agriculture 473 18.3 Metaverse 475 18.4 Augmented Reality (AR) 478 18.5 Virtual Reality (VR) 480 18.6 Mixed Reality (MR) 482 18.7 Agriculture Training Simulations 485 18.8 Metaverse in Agriculture Trainings 487 18.9 Conclusions 488 19 Sustainable Farming in the Digital Era: AI and IoT Technologies Transforming Agriculture 493 Arti Sonawane, Suvarna Patil and Atul Kathole 19.1 Introduction 494 19.2 Related Work 498 19.3 Discussion of Proposed Approach 503 19.4 Application 508 19.5 Advantages and Disadvantages of System 509 19.6 Conclusion 510 20 Precision Agriculture with Unmanned Aerial Vehicles 513 Suresh S., Sampath Boopathi, Elayaraja R., Velmurugan D. and Selvapriya R. 20.1 Introduction 514 20.2 Agri-UAV Construction and Controls 516 20.3 Applications of UAVs in Agriculture 519 20.4 Conclusion 529 References 530 Index 535

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Author Information

Roheet Bhatnagar, PhD is a professor in the Department of Computer Science and Engineering at Manipal University, Jaipur with over 22 years of academic and industry experience. He has published five books and over 100 research papers in reputed conferences and journals and given numerous talks, lectures, and keynote speeches at international conferences. His research interests include soft computing, data structure, remote sensing, and software engineering.   Chandan Kumar Panda, PhD is an assistant professor at Bihar Agricultural University, Sabour with over 8 years of academic and industry experience. He has published three books, 16 book chapters, and over 50 papers in international journals and conferences and completed a number of projects for the Indian Council of Agricultural Research. 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 Department of Artificial Intelligence at Kafrelsheikh University. He has published over 70 papers and conference presentations in top-tier journals and serves as a reviewer and editor for prestigious academic journals. He specializes in artificial intelligence, machine learning, pattern recognition, and classification.

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