Agricultural Insights from Space
Machine Learning Applications in Satellite Data Analysis
- 1st Edition - October 24, 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
Agricultural Insights from Space offers a comprehensive exploration of how geospatial technology and machine learning are transforming modern agriculture. From satellite data acquisition and soil mapping to crop classification, yield prediction, and irrigation optimization, this volume presents cutting-edge methods for advancing precision and sustainable farming.
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 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.
- 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.
Professionals, researchers, and experts directly involved in the field of artificial intelligence, Geospatial intelligence, Agricultural scientists, AI researchers and practitioners working on developing machine learning-based solutions for precision agriculture, scientists and engineers interested in leveraging AI techniques to address agricultural growth, productivity, planning and management
1. Overview to Geospatial Technology and Machine Learning in Agriculture
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 categorisation using fusion of Satellite Imagery and Machine Learning
9. Geo- AI for Irrigation Management Systems in a smart way
10. Geospatial based mapping and monitoring of Pest and Disease Outbreaks utilising Machine Learning
11. Amalgamation of Geospatial Technology and machine learning for Livestock Management
Contributors: Parisha Bankhwal, Sugandha Panwar, Swati Uniyal
12. Machine learning and Geospatial technology for Mapping of Agroforestry Systems
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. Optimizing Crop Classification with Machine Learning: Insights from a Case Study
18. Synthetic Data Generation Using Microwave Modelling with Efficient Application of Machine Learning for Bare land Soil Moisture Retrieval- A case Study
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 categorisation using fusion of Satellite Imagery and Machine Learning
9. Geo- AI for Irrigation Management Systems in a smart way
10. Geospatial based mapping and monitoring of Pest and Disease Outbreaks utilising Machine Learning
11. Amalgamation of Geospatial Technology and machine learning for Livestock Management
Contributors: Parisha Bankhwal, Sugandha Panwar, Swati Uniyal
12. Machine learning and Geospatial technology for Mapping of Agroforestry Systems
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. Optimizing Crop Classification with Machine Learning: Insights from a Case Study
18. Synthetic Data Generation Using Microwave Modelling with Efficient Application of Machine Learning for Bare land Soil Moisture Retrieval- A case Study
- Edition: 1
- Latest edition
- Published: October 24, 2025
- Language: English
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.
Affiliations and expertise
Senior Professor, Department of Electronics & Communication Engineering, Indian Institute of Technology, Roorkee, IndiaKC
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.
Affiliations and expertise
Associate Professor, School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, IndiaGY
Ghazaala Yasmin
Dr. Ghazaala Yasmin is an Assistant Professor (Senior Grade) in the Dept. of CSE & IT at Jaypee Institute of Information Technology (JIIT). She has also worked as Assistant Professor in the Department of Computer Science Engineering at St. Thomas’ College of Engineering and Technology, Kolkata. She has 8 years of teaching and research experience. She received her Ph.D. degree from Indian Institute of Engineering Science and Technology (IIEST), Shibpur at Department of Computer Science and Technology, West Bengal. She did her M.Tech from Calcutta University in Computer Science and Engineering. Her research interests are Computer Vision, audio and video processing Medical Imaging, Agricultura data analysis, Machine and Deep Learning, Data Mining, NLP. She has published several reputed SCI journals and IEEE transaction and also published in reputative International Conference papers in analysis field. She has organized SERB funded workshop and international conferences like CIPR.
Affiliations and expertise
Assistant Professor (Senior Grade), Department of CSE & IT, Jaypee Institute of Information Technology (JIIT), New Delhi, India