Section I: Understanding AI and Machine Learning 1. Introduction to AI and Machine Learning (Written by Dr. Pradhan) 2. Implementing AI and ML in Agriculture: From Conventional to Smart Agricultural Practices 3. Revolutionizing Sustainable Agriculture: The Artificial Intelligence Approach 4. Challenges of Future Nexus: Combinatorial Reasoning with Machine Learning for Sustainable Agricultural Development 5. Embracing Technology for Sustainable Agriculture: A Survey of Information Systems, Precision Agriculture, and Automation 6. Scope and adoption of Machine learning and Deep learning in remote sensing in agriculture 7.
Viability Study of Variable Rate Technology through Machine Learning 8. Market Impact Assessment of AI-Enabled Agricultural Technologies Utilizing SAR/Optical Data 9. Implication of Artificial Intelligence in sustainable and smart farming: 10. Understanding and performing a cost analysis of smart agriculture Section II: Application of AI and Machine Learning in Agricultural Scenarios 11. From Pixels to Fields: Leveraging SAR and Optical Imagery Integration for Crop Area Mapping 12. Monitoring Crop Development and Yield Estimation Through Satellite and UAV Imagery Analysis Using Artificial Intelligence and Machine Learning 13. An Image Processing Approach for Plant Disease Detection 14. Weather based Crop Yield Modeling and Prediction using Statistical and Machine Learning techniques: The state of the art 15.
Dynamic Crop Insights, Crop Dynamic Analytics: A Case Study of Real-Time Monitoring and Predictive Analytics for Corn and Soybean Growth 16. Efficient monitoring of agriculture fields using off-the-shelf satellite imagery. 17. Integrating Machine Vision Control to Spot Spraying System using Controller Area Network 18. Integrating IoT for Real-time Monitoring and Control in Smart Hydroponics Crop Production 19. 3D-ResNet-RNNs: Integrating Recurrent Neural Networks and 3D-ResNet for Enhanced Soybean Yield Predictions Using Multi-Modal Remote Sensing Data 20. Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine 21. Soil moisture monitoring using SAR polarimetry: A critical review 22.
A comprehensive review of the role of artificial intelligence and computer vision for post-harvest analysis of fruits 23. Timely animal intrusion detection: Protection of agricultural fields Section III: Application of AI and Machine Learning in Aquatic Scenarios 24. Optimizing Groundwater Recharge Estimation and Mapping with Google Earth Engine: A Case Study of the Mahanadi River Basin, India 25. Leveraging Artificial Intelligence for Enhanced Aquaculture Management: A Focus on Toxicity Monitoring in Fish Farming 26. Modeling growth of Catla (Catla Catla) fish using artificial neural network (ANN) 27. Utilizing Machine Learning for Fish Resource Management in Aquaculture 28. Water Quality Index Prediction through Artificial Intelligence.