Sustainable Farming through Machine Learning - Satpathy, Suneeta; Kumar Paikaray, Bijay; Yang, Ming;(ed.) - Prospero Internet Bookshop
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Sustainable Farming through Machine Learning

Enhancing Productivity and Efficiency
 
Edition number: 1
Publisher: CRC Press
Date of Publication:
 
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Short description:

Explores the transformative potential of ML technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how AI/ML can optimize resource management and improve overall productivity in farming practices.

Long description:

This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices.


Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies.


This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.

Table of Contents:

1. Exploring AI and ML Strategies for Crop Health Monitoring and Management. 2. Enhancing Crop Productivity by Suitable Crop Prediction Using Cutting-Edge Technologies. 3. Crop Yield Prediction Using Machine Learning Random Forest Algorithm. 4. A multi-objective based genetic approach for increasing crop yield on sustainable farming. 5. Drones For Crop Monitoring And Analysis. 6. Decision Support System For Sustainable Farming. 7. Empowering Agriculture: Harnessing the Potential of AI-Driven Virtual Tutors for Farmer Education and Investment Strategies. 8. Enhancing Agricultural Ecosystem Surveillance through Autonomous Sensor Networks. 9. Crop Disease Detection Using Image Analysis. 10. Automated Detection of Plant Diseases Utilizing Convolutional Neural Networks. 11. Apple Leaves Diseases Detection Using Deep Learning. 12.Optimizing Agricultural Yield: Comprehensive Approaches for Recommendation System in Precision Agriculture. 13. Advancements in Precision Agriculture: A Machine Learning-based Approach for Crop Management Optimization. 14. Precision Agriculture with Remote Sensing: Integrating Deep Learning for Crop Monitoring. 15. Farmers Guide: Data-Driven Crop Recommendations for Precision and Sustainable Agriculture Using IoT and ML. 16. Application of Machine Learning in the Analysis and Prediction of Animal Disease. 17. Transforming Indian Agriculture: A Machine Learning Approach for Informed Decision-Making and Sustainable Crop Recommendations. 18. Automated Detection of Water Quality for Smart Systems using Various Sampling Techniques - An Agricultural Perspective. 19. Scope of Artificial Intelligence (A.I.) in ?Agriculture Sector and its applicability in Farm Mechanization in Odisha. 20. Ethical Considerations and Social Implications.