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
Feeding a growing global population with a changing climate, shrinking arable land, and increasingly strained water resources is a challenge with no single solution. Machine Learn… Read more
Description
Description
Feeding a growing global population with a changing climate, shrinking arable land, and increasingly strained water resources is a challenge with no single solution. Machine Learning and AI Technology in Agricultural Applications recasts agriculture as fundamentally a problem of data acquisition, integration, and analysis. Drawing on concrete methods and case studies, the book shows how advanced technologies turn scattered sensor readings, satellite imagery, and field records into decision-making tools that enable more precise, more resilient, and more sustainable farming practices.
The chapters cover the full agricultural cycle: crop and weather indicators feeding models that predict yield before harvest; satellites and drones replacing manual field-monitoring surveys across the growing season; and image-based algorithms supporting targeted interventions, from detecting a diseased plant to directing a sprayer to apply treatment only where it is required. The same reliance on remote sensing and predictive modeling carries into aquaculture and water management, where AI and ML are used to estimate groundwater recharge, track fish growth, and monitor water quality. A dedicated set of chapters also examines the economic dimensions of this shift, assessing the viability, market impact, and costs of adopting these innovations, and clarifying where they are most likely to reshape how these sectors operate.
Together, these technical perspectives make the book a valuable resource for students building a foundation in this field, as well as for researchers and practitioners looking to apply its findings and insights to their own work.
The chapters cover the full agricultural cycle: crop and weather indicators feeding models that predict yield before harvest; satellites and drones replacing manual field-monitoring surveys across the growing season; and image-based algorithms supporting targeted interventions, from detecting a diseased plant to directing a sprayer to apply treatment only where it is required. The same reliance on remote sensing and predictive modeling carries into aquaculture and water management, where AI and ML are used to estimate groundwater recharge, track fish growth, and monitor water quality. A dedicated set of chapters also examines the economic dimensions of this shift, assessing the viability, market impact, and costs of adopting these innovations, and clarifying where they are most likely to reshape how these sectors operate.
Together, these technical perspectives make the book a valuable resource for students building a foundation in this field, as well as for researchers and practitioners looking to apply its findings and insights to their own work.
Key features
Key features
- Bridges theory and practice by distilling complex analytical approaches into practical, implementation-ready workflows.
- Surveys established ML models — such as random forests, support vector machines, and decision trees — alongside newer deep learning architectures, helping readers match the right tool to the task at hand.
- Demonstrates the value of real- or near-real-time monitoring, from continuous field analytics to automated control systems that respond to conditions as they change.
Readership
Readership
Agricultural researchers, agri-tech developers, data scientists, and academic institutions seeking a rigorous, application-driven reference on AI and ML solutions for modern sustainable agriculture.
Table of contents
Table of contents
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
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
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.
Affiliations and expertise
Associate Professor, Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati University, Sriniketan, Birbhum, West Bengal, IndiaCS
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.Affiliations and expertise
Researcher, Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati University, Sriniketan, Birbhum, West Bengal, IndiaSS
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.
Affiliations and expertise
Founder and Director, Prajukti Research Pvt. Ltd., Baruipur, Kolkata, West Bengal, IndiaAM
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.
Affiliations and expertise
Research Staff, Ludwig Franzius Institute of Hydraulic, Estuarine and Coastal Engineering, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hannover, Hannover, GermanyQP
Quoc Bao Pham
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.
Affiliations and expertise
Assistant Professor, Institute of Earth Sciences, Faculty of Natural Sciences, University of Silesia in Katowice, Sosnowiec, PolandBP
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.
Affiliations and expertise
Professor and Director, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, Australia