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.
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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
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Explores how satellite data and machine learning can contribute to more efficient resource management, upgrade crop productivity, and environmental conservation
Addresses applications, potential limitations, and challenges in implementing AI-based solutions for agriculture
Presents a balanced perspective on the benefits associated with AI and geospatial technology
Emphasizes the importance of responsible and transparent Geo-AI development to ensure equitable and sustainable outcomes
Uses an interdisciplinary approach to bridge the gap between AI and agriculture science
Offers practical insights and guidance on leveraging AI to create intelligent, smart, and more sustainable environment for agricultural activities
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Produktdetaljer
ISBN
9780443341137
Publisert
2025-10-01
Utgiver
Elsevier Science Publishing Co Inc; Academic Press Inc
Høyde
229 mm
Bredde
152 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
300