Agricultural Insights from Space : Machine Learning Applications in Satellite Data Analysis

著者名：Singh Ph.D., Dharmendra (EDT)/Chaurasia Ph.D., Kuldeep (EDT)/Yasmin Ph.D., Ghazaala (EDT)

Academic Press（2025/11/17発売）

• 言語：ENG
• ISBN：9780443341137
• eISBN：9780443341144

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.- Critically examines real-world constraints and considerations in deploying AI-driven agricultural technologies, helping readers anticipate implementation challenges and develop more resilient, context-aware solutions.- Delivers a nuanced analysis of both opportunities and trade-offs, enabling readers to make informed decisions about adopting geospatial and AI tools in diverse agricultural settings.- Considers ethical, social, and environmental dimensions of geo-AI development, equipping readers to design and advocate for responsible innovations that promote equity and long-term sustainability in food systems.

Table of Contents

1. Overview of Geospatial Technology and Machine Learning in Agriculture2. Spatial Data Acquisition Methods for Agricultural Monitoring3. Machine Learning Techniques for Crop Identification and Classification4. Predictive Modeling and Analysis of Crop Yield and Productivity5. Integration of Geospatial Technology and Machine Learning for Precision Agriculture6. Crop Health Monitoring using Geospatial Methods and Deep Learning7. Integrating Climate Data for Agricultural Resilience using Geospatial Approaches8. Soil Mapping and Categorization Using Fusion of Satellite Imagery and Machine Learning9. Geo-artificial Intelligence for Smart Irrigation Management Systems10. Geospatial-based Mapping and Monitoring of Pest and Disease Outbreaks Utilizing Machine Learning11. Integration of Geospatial Technology and Machine Learning for Livestock Management12. Machine Learning and Geospatial Technology for Agroforestry System Mapping13. Geospatial and Machine Learning-based Mapping and Analysis for Agricultural Sustainability14. Deep Learning and Geospatial Technology-based Decision Support Systems for Smart Agricultural and Irrigation Applications15. A Case Study on Lagrange Polynomials and Machine Learning for Yield Prediction16. Leveraging Deep Learning Ensembles for Rice Disease Classification: A Case Study17. A Case Study on Optimizing Crop Classification with Machine Learning18. Synthetic Data Generation Using Microwave Modeling with Efficient Application of Machine Learning for Bare Land Soil Moisture Retrieval: A Case Study