Agricultural Insights from Space: Machine Learning Applications in Satellite Data Analysis seamlessly integrates theoretical knowledge with practical applications, presenting cutting-edge research alongside real-world examples. This book leverages geospatial technology and Artificial Intelligence to address various challenges in agriculture. Readers will find practical examples and case studies demonstrating how machine learning and deep learning techniques can extract valuable insights from remote sensing data, optimizing agricultural processes. Highlighting the significance of satellite data, the book explores the benefits of leveraging space-based information for enhancing agricultural practices.
The book emphasizes the importance of geospatial intelligence and AI technologies in monitoring and managing agricultural activities. It inspires readers to envision a future where these innovative approaches lead to more productive agricultural environments and healthier growth for future generations.
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
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. Case Studies, Best Practices, implementation challenges and opportunities in the adoption of Geospatial Technology and Machine Learning approaches in agricultural domain
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