Machine Learning and AI Technology for Agricultural Applications
- 1 Edición - 1 de noviembre de 2026
- Última edición
- Editores: Kishore Chandra Swain, Chiranjit Singha, Satiprasad Sahoo, Armin Moghimi, Quoc Bao Pham, Biswajeet Pradhan
- Idioma: Inglés
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… Leer más
Descripción
Descripción
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 artificial intelligence (AI) and machine learning (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 artificial intelligence (AI) and machine learning (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.
Puntos claves
Puntos claves
- 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.
De interès para
De interès para
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.
Índice
Índice
Section I: Understanding artificial intelligence and machine learning
1. Introduction to artificial intelligence and machine learning
2. Implementing artificial intelligence and machine learning in agriculture: from conventional to smart agricultural practices
3. Revolutionizing sustainable agriculture: the artificial intelligence approach
4. An introduction to the challenges of the future nexus: optimization through 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. Implications of artificial intelligence in sustainable and smart farming
10. Techniques for understanding and performing cost analysis in smart agriculture
Section II: Applications of artificial intelligence and machine learning in agriculture
11. From pixels to fields: leveraging SAR and optical imagery integration for crop area mapping
12. Improving crop growth monitoring through artificial intelligence-based remote sensing applications
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: real-time monitoring and predictive analytics for corn and soybean growth
16. Efficient monitoring of agricultural fields using off-the-shelf satellite imagery
17. Integrating machine vision control into a spot-spraying system using a controller area network
18. Integrating the Internet of Things for real-time monitoring and control in smart hydroponic crop production
19. 3D-ResNet-RNNs: integrating recurrent neural networks and 3D-ResNet for enhanced soybean yield predictions using multimodal remote sensing data
20. Crop-Net: a novel deep learning framework for crop classification using time-series Sentinel-1 imagery with 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 in postharvest fruit applications
23. Timely animal intrusion detection for the protection of agricultural fields
Section III: Applications of artificial intelligence and machine learning in aquatic systems
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) using an artificial neural network
27. Utilizing machine learning for fish resource management in aquaculture
28. Water quality index prediction using artificial intelligence in the Dulung River basin
29. Assessment of land surface temperature dynamics in the Bhyundar Ganga catchment using landsat data: implications for vegetation cover monitoring
1. Introduction to artificial intelligence and machine learning
2. Implementing artificial intelligence and machine learning in agriculture: from conventional to smart agricultural practices
3. Revolutionizing sustainable agriculture: the artificial intelligence approach
4. An introduction to the challenges of the future nexus: optimization through 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. Implications of artificial intelligence in sustainable and smart farming
10. Techniques for understanding and performing cost analysis in smart agriculture
Section II: Applications of artificial intelligence and machine learning in agriculture
11. From pixels to fields: leveraging SAR and optical imagery integration for crop area mapping
12. Improving crop growth monitoring through artificial intelligence-based remote sensing applications
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: real-time monitoring and predictive analytics for corn and soybean growth
16. Efficient monitoring of agricultural fields using off-the-shelf satellite imagery
17. Integrating machine vision control into a spot-spraying system using a controller area network
18. Integrating the Internet of Things for real-time monitoring and control in smart hydroponic crop production
19. 3D-ResNet-RNNs: integrating recurrent neural networks and 3D-ResNet for enhanced soybean yield predictions using multimodal remote sensing data
20. Crop-Net: a novel deep learning framework for crop classification using time-series Sentinel-1 imagery with 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 in postharvest fruit applications
23. Timely animal intrusion detection for the protection of agricultural fields
Section III: Applications of artificial intelligence and machine learning in aquatic systems
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) using an artificial neural network
27. Utilizing machine learning for fish resource management in aquaculture
28. Water quality index prediction using artificial intelligence in the Dulung River basin
29. Assessment of land surface temperature dynamics in the Bhyundar Ganga catchment using landsat data: implications for vegetation cover monitoring
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 1 de noviembre de 2026
- Idioma: Inglés
Sobre los editores
Sobre los editores
KS
Kishore Chandra Swain
Kishore Chandra Swain received his master's and PhD from the Asian Institute of Technology (AIT), Thailand. His postdoctoral experience includes 2 years in Denmark and 1 year at Dalhousie University, Canada. He has published six books and nearly 100 journal and conference proceedings papers. He has sound computer programming knowledge in C, C++, MATLAB, AutoCAD, ArcView, ArcGIS, ENVI, and SNAP/GEE. Four students have been awarded PhDs under his supervision. His major areas of research have been precision agriculture, computer vision, machine learning, and flood and drought monitoring.
Afiliaciones y experiencia
Associate Professor, Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati University, Sriniketan, Birbhum, West Bengal, IndiaCS
Chiranjit Singha
Chiranjit Singha received his PhD in agricultural engineering from Visva-Bharati 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 (2013–15) in India and the Best Research Paper Award at various international seminars. Additionally, he has reviewed articles for numerous prestigious international journals.
Afiliaciones y experiencia
Researcher, Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati University, Sriniketan, Birbhum, West Bengal, IndiaSS
Satiprasad Sahoo
Satiprasad Sahoo is the founder and director of Prajukti Research Pvt. Ltd. in Baruipur, Kolkata, India. He also worked as a water resource engineer at the International Centre for Agricultural Research in Dry Areas (ICARDA), Egypt. He received a BSc in geography from the University of Calcutta, Kolkata, India, in 2009, an MSc in remote sensing and GIS from Vidyasagar University, Midnapore, India, in 2011, and an MSc in geography from Chhatrapati Shahu Ji Maharaj University, Kanpur, India, in 2013. Furthermore, he received an MS (by research) in water management from the School of Water Resources at the Indian Institute of Technology Kharagpur, West Bengal, India, in 2016. He completed a PhD in hydro-environmental modeling from Jadavpur University, West Bengal, India, in 2019. He worked on postdoctoral research at the Indian Institute of Technology, Guwahati, Assam, India, and Nalanda University, Bihar, India. He has worked as a project officer, water resource engineer, assistant professor, and guest faculty at several institutions.
Afiliaciones y experiencia
Founder and Director, Prajukti Research Pvt. Ltd., Baruipur, Kolkata, West Bengal, IndiaAM
Armin Moghimi
Armin Moghimi received his MS and PhD 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, Hannover, Germany, 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.
Afiliaciones y experiencia
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 PhD from the Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan, 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 for 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.
Afiliaciones y experiencia
Assistant Professor, Institute of Earth Sciences, Faculty of Natural Sciences, University of Silesia in Katowice, Sosnowiec, PolandBP
Biswajeet Pradhan
Biswajeet Pradhan is a distinguished professor at the School of Civil and Environmental Engineering, University of Technology Sydney, Australia. He is an international expert in data-driven modeling and a pioneer in combining spatial modeling with statistical and machine learning models for natural hazard predictions, including landslides. He has a strong research record, with more than 600 journal articles. He is an interdisciplinary researcher whose publications span multiple research areas, including geological and geo-environmental engineering.
Afiliaciones y experiencia
Professor and Director, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia