Agricultural Insights from Space

Machine Learning Applications in Satellite Data Analysis
Erscheinungsjahr 2025
ISBN: 978-0-443-34113-7
Verlag: Elsevier Science

Buch, Englisch, 486 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

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.

Autoren/Hrsg.

Singh, Dharmendra

Chaurasia, Kuldeep

Yasmin, Ghazaala

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor Agrarökonomie, Ernährungswirtschaft
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Agrartechnik, Landmaschinen
• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Satellitenkommunikation, Satellitentechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Overview of 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 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

Über Autor(innen)

Yasmin, Ghazaala
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.

Chaurasia, Kuldeep
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.

Singh, Dharmendra
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.