Agricultural Insights from Space
Machine Learning Applications in Satellite Data Analysis
- 1st Edition - November 12, 2025
- Latest edition
- Editors: Dharmendra Singh, Kuldeep Chaurasia, Ghazaala Yasmin
- Language: English
Agricultural Insights from Space offers a comprehensive exploration of how geospatial technology and machine learning are transforming modern agriculture. From satellite dat… Read more
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Description
Description
Key chapters highlight the integration of spatial data with AI to monitor crop health, track pest and disease outbreaks, manage livestock, and map agroforestry systems. The use of climate data and deep learning models illustrates how these innovations strengthen resilience and support informed decision-making in the face of environmental challenges.
Through detailed methodologies and real-world case studies, including applications of Lagrange polynomials, deep learning ensembles, and synthetic data generation, the book showcases practical solutions that bridge research and implementation.
Whether applied in academic research, fieldwork, or technology development, Agricultural Insights from Space offers a multidisciplinary foundation for tackling complex agricultural challenges. It empowers readers to harness emerging technologies not just to improve efficiency, but to reshape agricultural systems for long-term sustainability and impact.
Key features
Key features
- Critically examines real-world constraints and considerations in deploying AI-driven agricultural technologies, helping readers anticipate implementation challenges and develop more resilient, context-aware solutions.
- Delivers a nuanced analysis of both opportunities and trade-offs, enabling readers to make informed decisions about adopting geospatial and AI tools in diverse agricultural settings.
- Considers ethical, social, and environmental dimensions of geo-AI development, equipping readers to design and advocate for responsible innovations that promote equity and long-term sustainability in food systems.
Readership
Readership
Table of contents
Table of contents
2. Spatial Data Acquisition Methods for Agricultural Monitoring
3. Machine Learning Techniques for Crop Identification and Classification
4. Predictive Modeling and Analysis of Crop Yield and Productivity
5. Integration of Geospatial Technology and Machine Learning for Precision Agriculture
6. Crop Health Monitoring using Geospatial Methods and Deep Learning
7. Integrating Climate Data for Agricultural Resilience using Geospatial Approaches
8. Soil Mapping and Categorization Using Fusion of Satellite Imagery and Machine Learning
9. Geo-artificial Intelligence for Smart Irrigation Management Systems
10. Geospatial-based Mapping and Monitoring of Pest and Disease Outbreaks Utilizing Machine Learning
11. Integration of Geospatial Technology and Machine Learning for Livestock Management
12. Machine Learning and Geospatial Technology for Agroforestry System Mapping
13. Geospatial and Machine Learning-based Mapping and Analysis for Agricultural Sustainability
14. Deep Learning and Geospatial Technology-based Decision Support Systems for Smart Agricultural and Irrigation Applications
15. A Case Study on Lagrange Polynomials and Machine Learning for Yield Prediction
16. Leveraging Deep Learning Ensembles for Rice Disease Classification: A Case Study
17. A Case Study on Optimizing Crop Classification with Machine Learning
18. Synthetic Data Generation Using Microwave Modeling with Efficient Application of Machine Learning for Bare Land Soil Moisture Retrieval: A Case Study
Product details
Product details
- Edition: 1
- Latest edition
- Published: November 12, 2025
- Language: English
About the editors
About the editors
DS
Dharmendra Singh
Dharmendra Singh is Senior Professor in Electronics and Communication Engineering Department, Indian Institute of Technology Roorkee, Roorkee, India and a Senior Member of IEEE with more than 27 years of experience of teaching and research. He has received many international awards and recognition, as well as the best innovation award in India Mobile Congress for the development of Satellite Based Agriculture Information System, and the best Industrial Research award by Institution of Engineers, Roorkee Chapter. He has twice received the National GOLD Award for e-governance for Outstanding research on Citizen Centric Services and has ranked among the top 2% scientists of the world in the field of Electronics and Telecommunication, by independent study done by Stanford University.
He has published extensively and developed several products including those releated to Technology for Stealth Material, Agriculture Information System, Through the wall imaging system, ground penetrating radar, Radomes, etc. His main research interests involve microwave/mm wave imaging and numerical modeling, radar absorbing materials, stealth application, Artificial Intelligence, Computer Vision, Deep Learning, Machine Learning, Data Fusion, ICT, Satellite data application, polarimetric and interferometric application of microwave data. He is also the Coordinator of DRONE RESEARCH CENTER, IIT Roorkee.
KC
Kuldeep Chaurasia
Dr. Kuldeep is currently working as Associate Professor in the School of Computer Science Engineering and Technology, Bennett University, India. Dr. Kuldeep is a highly accomplished scholar, having earned his doctoral degree in Geomatics from the prestigious Indian Institute of Technology Roorkee, India. He has also made significant contributions as a research scientist in the implementation of national projects, working with Regional Centre, National Remote Sensing Centre, ISRO, Dept. of space, Hyderabad, India. Dr. Kuldeep's expertise lies in the application of Machine learning and Deep learning in geospatial domain, LULC mapping, flood mapping and modelling, Spatial Data Management, Computer Networks and Geo-Blockchain. He has published many research papers in reputed international journals/conferences. His passion for these areas of research has led to many breakthroughs in the field, making him a highly respected and sought-after expert in the academic community.
GY