Agricultural Insights from Space
Machine Learning Applications in Satellite Data Analysis
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Product details:
- Publisher Elsevier Science
- Date of Publication 24 October 2025
- ISBN 9780443341137
- Binding Paperback
- No. of pages300 pages
- Size 229x152 mm
- Weight 450 g
- Language English 700
Categories
Long description:
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.
Table of Contents:
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
