Agricultural Insights from Space -

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Résumé :
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....

Sommaire:
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...