Machine Learning and AI Technology for Agricultural Applications

Prof. Swain received his Masters and Ph.D. from Asian Institute of Technology(AIT), Thailand. His postdoctoral experience includes Denmark (2 years) and Dalhousie University, Canada (1 year). He has published six books and nearly 100 journal and conference proceedings papers. He have sound computer programming knowledge in C, C++, MatLab, AutoCAD, Arcview, ArcGIS, ENVI, SNAP/GEE. Four students have been awarded Ph.D. under his supervision. His major area of research has been precision agriculture, computer vision, machine learning, flood and drought monitoring etc. Dr. Chiranjit Singha received his Ph.D. in Agricultural Engineering from Visva Bharati University (Central University), West Bengal, India, in 2019. His primary research focuses on applying Precision Agriculture (PA), Geographic Information Systems (GIS), and Remote Sensing (RS) integrated with Machine Learning (ML) and Deep Learning (DL) to ecological environments and disaster management. His work aims to deepen the understanding of Earth observation system science, particularly in the context of geo-environmental and hydrometeorological/climate change dynamics.He has received several accolades, including the UGC Junior Research Fellowship (2013–2015) in India and the Best Research Paper Award at various international seminars. Additionally, he has reviewed articles for numerous prestigious international journals. Dr. Satiprasad Sahoo is the Founder and Director of Prajukti Research Pvt Ltd in Baruipur, Kolkata. He also worked as a water resource engineer at the International Centre for Agricultural Research in Dry Areas (ICARDA), Egypt. He received a B.Sc. in geography from the University of Calcutta in 2009, an M.Sc. in remote sensing and GIS from Vidyasagar University in 2011, and an M.Sc. in geography from C.S.J.M University in 2013. Furthermore, he received an M.S. (by research) in Water Management from the School of Water Resources at the Indian Institute of Technology Kharagpur in 2016. He completed a Ph.D. in hydro-environmental modeling from Jadavpur University in 2019. He worked on postdoctoral research at the Indian Institute of Technology, Guwahati, and Nalanda University. He has worked as a project officer, water resource engineer, assistant professor, and guest faculty at several institutions. Arrmin Moghimi received his M.S. and Ph.D. degrees in Civil Photogrammetry and Remote Sensing Engineering from K. N. Toosi University of Technology, Tehran, Iran, in 2015 and 2022, respectively. Since 2023, he has been a Postdoctoral Research Associate at the Ludwig Franzius Institute for Hydraulic, Estuarine, and Coastal Engineering, part of the Faculty of Civil Engineering and Geodesy at Leibniz University Hannover, Germany. Dr. Moghimi’s research interests encompass a wide range of topics, including computer vision, explainable artificial intelligence, agricultural crop yield prediction and classification, remote sensing, photogrammetry, deep learning, change detection, image registration, machine learning, SAR image processing, and LiDAR data processing. He serves as an Associate Editor for The Photogrammetric Record published by Wiley Online Library. Dr. Moghimi has authored over 50 publications and reviewed more than 250 peer-reviewed journal papers. He actively collaborates on multidisciplinary research projects, contributing to the design and execution of innovative programs. Quoc Bao Pham obtained his Ph.D. from the Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Taiwan. He has more than 12 years of research experience in hydrology, GIS, Remote Sensing, and applied soft computing. His research interests are water resources engineering, applied machine learning, climate change, statistical downscaling, hydrology, remote sensing, GIS, and spatial analysis. He has collaborated actively with many international researchers in several other disciplines of engineering. He has published more than 80 SCI/SCIE research papers and is a reviewer of more than 20 SCI/SCIE journals. He is also a guest editor of some SCI/SCIE journals in the fields of water resources, remote sensing, and machine learning. Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability.

• Section I: Understanding AI and Machine Learning1. Introduction to AI and Machine Learning 2. Implementing AI and ML in Agriculture: From Conventional to Smart Agricultural Practices3. Revolutionizing Sustainable Agriculture: The Artificial Intelligence Approach4. Challenges of Future Nexus: Combinatorial Reasoning with Machine Learning for Sustainable Agricultural Development5. Embracing Technology for Sustainable Agriculture: A Survey of Information Systems, Precision Agriculture, and Automation6. Scope and adoption of Machine learning and Deep learning in remote sensing in agriculture7. Viability Study of Variable Rate Technology through Machine Learning8. Market Impact Assessment of AI-Enabled Agricultural Technologies Utilizing SAR/Optical Data9. Implication of Artificial Intelligence in sustainable and smart farming:10. Understanding and performing a cost analysis of smart agricultureSection II: Application of AI and Machine Learning in Agricultural Scenarios11. From Pixels to Fields: Leveraging SAR and Optical Imagery Integration for Crop Area Mapping12. Monitoring Crop Development and Yield Estimation Through Satellite and UAV Imagery Analysis Using Artificial Intelligence and Machine Learning13. An Image Processing Approach for Plant Disease Detection14. Weather based Crop Yield Modeling and Prediction using Statistical and Machine Learning techniques: The state of the art15. Dynamic Crop Insights, Crop Dynamic Analytics: A Case Study of Real-Time Monitoring and Predictive Analytics for Corn and Soybean Growth16. Efficient monitoring of agriculture fields using off-the-shelf satellite imagery.17. Integrating Machine Vision Control to Spot Spraying System using Controller Area Network18. Integrating IoT for Real-time Monitoring and Control in Smart Hydroponics Crop Production19. 3D-ResNet-RNNs: Integrating Recurrent Neural Networks and 3D-ResNet for Enhanced Soybean Yield Predictions Using Multi-Modal Remote Sensing Data20. Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine21. Soil moisture monitoring using SAR polarimetry: A critical review22. A comprehensive review of the role of artificial intelligence and computer vision for post-harvest analysis of fruits23. Timely animal intrusion detection: Protection of agricultural fieldsSection III: Application of AI and Machine Learning in Aquatic Scenarios24. Optimizing Groundwater Recharge Estimation and Mapping with Google Earth Engine: A Case Study of the Mahanadi River Basin, India25. Leveraging Artificial Intelligence for Enhanced Aquaculture Management: A Focus on Toxicity Monitoring in Fish Farming26. Modeling growth of Catla (Catla Catla) fish using artificial neural network (ANN)27. Utilizing Machine Learning for Fish Resource Management in Aquaculture28. Water Quality Index Prediction through Artificial Intelligence

Select Guide Rating