Harvesting Intelligence: The AI Revolution in Agriculture - From Fields to Algorithms, Cultivating Future Harvests

This edited volume explores the transformative impact of Artificial Intelligence on agriculture, a sector critical to economic development and global food security. As modern agriculture is undergoing a paradigm shift by integrating advanced technologies such as AI, robotics, computer vision, the Internet of Things (IoT), and data analytics across various farming processes, this volume aims to enhance productivity and sustainability.
The role of AI in agriculture holds vast potential for increasing yields, optimizing resource allocation, and minimizing environmental impact. By utilizing data-driven insights, farmers can make informed decisions on key factors like irrigation, crop management, and livestock care, driving a future of sustainable farming that supports global food security. The widespread adoption of AI is set to revolutionize the industry, creating a resilient agricultural ecosystem. This book provides an in-depth analysis of AI applications across sub-domains such as crops, livestock, fisheries and related data issues. It features real-world case studies and explores key technological areas, including computer vision, remote sensing, large language models, natural language processing, IoT, and machine learning. Grouped into three sections—(i) AI in Agriculture Management and Precision Farming, (ii) AI in Livestock and Fisheries Management and (iii) Sustainable Practices, Open Data Ecosystem and Policy Issues — the book highlights how AI is reshaping the future of agriculture, fostering a smarter and more sustainable agricultural ecosystem.
This volume is essential for researchers, students, and professionals in agricultural studies and related fields. It offers valuable insights for farmers and extension workers seeking to adopt innovative technologies.

List of contents

Section 1. AI in Agriculture Management and Precision Farming.- Chapter 1. AgriNet-Light: Unlocking the Power of Lightweight AI models for Agriculture.- Chapter 2. AI-Enabled UGV and UAVs in Row-Crop Production Agriculture.- Chapter 3. e-Crop based smart farming for another boom in agricultural production.- Chapter 4. Computer Vision-based Object Detection for High Throughput Plant Phenotyping.- Chapter 5. Deep Learning models for crop protection: A case study  of wheat.- Chapter 6. AI-DISC: An Intelligent Tool for Disease and Pests Detection in Crops.- Chapter 7. Deep Learning-Based Computer Vision Methods for Smart Weed Identification.- Chapter 8. Digital Entomology: Revolutionizing Biodiversity Management in Indian Agriculture.- Chapter 9. IoT and AI integrated Robots for Site Specific Weed Management.- Section 2:  AI in Livestock and Fisheries Management.- Chapter 10. Leveraging Artificial Intelligence for the Advancement of Animal Sciences: Innovations, Applications, and Impacts.- Chapter 11. SHRIA: Natural Language Processing based Chatbot Application for Effective Livestock Management.- Chapter 12. AI-DISA: An Artificial Intelligence-based Disease Identification System for Livestock Health Management.- Chapter 13. AI/ML in molecular epidemiology of transboundary infectious animal virus with special reference to Foot-and-mouth Disease.- Chapter 14. Artificial Intelligence for the Blue Revolution: Advancing Fisheries and Aquaculture Management.- Section 3: Sustainable Practices, Open Data Ecosystem  and Policy Issues.- Chapter 15. Recent Advances in Deep Learning with Applications in Data Fusion and Agriculture.- Chapter 16. Leveraging Artificial Intelligence for Agricultural Knowledge Dissemination: The Krishi-Mantrana Question Answering System.- Chapter 17. Large Language Models in Agriculture: From Theory to Practice.- Chapter 18. Upscaling Digital Agriculture in India: Strategies for Wider Adoption.

About the author

Dr. Alka Arora is working as Professor(Computer Applications) and Principal Scientist at ICAR-Indian Agricultural Statistics Research Institute (IASRI), a leading institute in Statistics, Computer Applications, and Bioinformatics under ICAR. With over 27 years of experience in agricultural research, education, and extension, her expertise spans AI, deep learning, image analysis, web and mobile app development, and databases. She has contributed significantly towards development of several national-level e-governance and informatics applications. As a faculty member at ICAR-IARI, she has guided numerous doctoral and postgraduate students. Dr. Arora has co-edited two books, served as Editor for a special journal issue on “AI Applications in Agriculture,” published over 50 research papers, and delivered many invited talks at national and international forums.
Dr. Sudeep Marwaha, Former Head and Professor at ICAR-IASRI, has over 25 years of experience in research, teaching, training, and extension. He has led 20+ major IT initiatives, including World Bank–funded projects, and served as Principal Investigator for key projects like the Education Portal, NAHEP, RAES, and AI-based maize disease advisory systems. He has developed 50+ online systems and mobile apps, widely used by agricultural universities, KVKs, and millions of farmers, with several securing copyrights. His expertise includes knowledge-based systems, ontologies, deep learning, and AI-driven tools for crop and livestock disease identification. He has conducted numerous national training programs, published 50+ research papers in reputed journals and conferences, and mentored many MSc and Ph.D. students in Computer Applications.
Dr. Rajni Jain is a seasoned researcher and academic specializing in the application of computer science in agriculture. Holding a Ph.D. from Jawaharlal Nehru University, she currently serves as a Principal Scientist at ICAR-NIAP, India. Her work focuses on AI-based models, decision support systems, crop planning, and agricultural information systems. She has significantly contributed to data mining, productivity analysis, and ICT in agriculture. Dr. Jain has published extensively, guided postgraduate and doctoral students at ICAR-IARI, and authored three books. She also edited a special journal issue on AI in agriculture and organized several national and international conference sessions to promote knowledge sharing. Her dedication to advancing agricultural innovation through Artificial Intelligence is evident in her multifaceted roles as a researcher, editor, and facilitator of knowledge dissemination in the agricultural community.
Dr. Rajender Parsad, Former Director of ICAR-IASRI, New Delhi, is a renowned expert in Agricultural Statistics and statistical computing, known for his impactful research both nationally and internationally. His work covers both theoretical and applied statistics, focusing on advanced methodologies to support agricultural research. A key advocate for human resource development in this field, he has introduced modern experimental designs and analytical tools within the National Agricultural Research and Education System. He played a pivotal role in developing online learning platforms, e-advisory systems, service-oriented computing, and centralized research data repositories. Dr. Parsad has received numerous honors, including the National Award in Statistics in honor of Prof. C.R. Rao, the Gold Icon Award in Open Data Championship Category from Govt. of India (2020), and Fellow of NAAS. Often referred to as the "Data Man of ICAR," he has chaired many national committees and served as a consultant to ICARDA. He has authored 200+ research papers with over 175 co-authors, contributed to books, e-books, monographs, and developed several statistical tools (R packages, SAS macros). He has mentored numerous research students and holds various editorial positions, underscoring his lasting impact on Statistical Sciences.

Summary

This edited volume explores the transformative impact of Artificial Intelligence on agriculture, a sector critical to economic development and global food security. As modern agriculture is undergoing a paradigm shift by integrating advanced technologies such as AI, robotics, computer vision, the Internet of Things (IoT), and data analytics across various farming processes, this volume aims to enhance productivity and sustainability.
The role of AI in agriculture holds vast potential for increasing yields, optimizing resource allocation, and minimizing environmental impact. By utilizing data-driven insights, farmers can make informed decisions on key factors like irrigation, crop management, and livestock care, driving a future of sustainable farming that supports global food security. The widespread adoption of AI is set to revolutionize the industry, creating a resilient agricultural ecosystem. This book provides an in-depth analysis of AI applications across sub-domains such as crops, livestock, fisheries and related data issues. It features real-world case studies and explores key technological areas, including computer vision, remote sensing, large language models, natural language processing, IoT, and machine learning. Grouped into three sections—(i) AI in Agriculture Management and Precision Farming, (ii) AI in Livestock and Fisheries Management and (iii) Sustainable Practices, Open Data Ecosystem and Policy Issues — the book highlights how AI is reshaping the future of agriculture, fostering a smarter and more sustainable agricultural ecosystem.
This volume is essential for researchers, students, and professionals in agricultural studies and related fields. It offers valuable insights for farmers and extension workers seeking to adopt innovative technologies.

Product details

• Assisted by: Alka Arora (Editor), Rajni Jain (Editor), Rajni Jain et al (Editor), Sudeep Marwaha (Editor), Rajender Parsad (Editor)
• Publisher: Springer EN
• Languages: English
• Product format: Hardback
• Release: 15.02.2026
• EAN: 9789819551743
• ISBN: 978-981-9551-74-3
• Illustrations: Approx. 350 p. 50 illus., 30 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
• Subjects: Natural sciences, medicine, IT, technology > Biology > Agriculture, horticulture; forestry, fishing, food; Künstliche Intelligenz, machine learning, Maschinelles Lernen, Artificial Intelligence, Internet of things, Agriculture, Machine Learning Applications, Precision Farming, Smart Farming Solutions, AI based Digital Agriculture