Agricultural Insights from Space by Singh, Dharmendra; Chaurasia, Kuldeep; Yasmin, Ghazaala;

Hosszú leírás:

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.

Tartalomjegyzék:

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