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Full Description
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.
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.
