Machine Learning and AI Technology for Agricultural Applications
- 1st Edition - November 1, 2026
- Latest edition
- Editors: Kishore Chandra Swain, Chiranjit Singha, Satiprasad Sahoo, Armin Moghimi, Quoc Bao Pham, Biswajeet Pradhan
- Language: English
Machine Learning and AI Technology in Agricultural Applications offers a comprehensive overview of how artificial intelligence and machine learning are transforming the agricu… Read more
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Description
Description
Beyond introducing core machine learning models such as random forest, support vector machines, logistic regression, and decision trees, the book highlights the centralization of critical agricultural data in the cloud. This resource benefits both students and seasoned agricultural scientists, providing practical insights for optimizing crop yields, monitoring soil and weather conditions, and managing resources like fertilizers and pesticides. The book also explores the rapid analysis of complex datasets, empowering users to make informed, timely decisions in real-world agricultural scenarios.
Key features
Key features
- Connects core concepts of AI and ML to real-world application practices in agriculture
- Explores applications in crop scouting and optimizing variable rate crop input
- Demonstrates the value in real or near real-time crop monitoring, yield estimation, and prediction
- Highlights SAR/optical data applications and analysis in agriculture
Readership
Readership
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: November 1, 2026
- Language: English
About the editors
About the editors
KS
Kishore Chandra Swain
CS
Chiranjit Singha
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.SS
Satiprasad Sahoo
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.AM
Armin Moghimi
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.
QP
Quoc Bao Pham
BP
Biswajeet Pradhan
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.