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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. Preface - p. xvii 8. Acknowledgments - p. xix 9. Chapter one. Overview of geospatial technology and machine learning in agriculture - p. 1 10. Chapter two. Spatial data acquisition methods for agricultural monitoring - p. 29 11. Chapter three. Machine learning techniques for crop identification and classification - p. 49 12. Chapter four. Predictive modeling and analysis of crop yield and productivity - p. 65 13. Chapter five. Integration of geospatial technology and machine learning for precision agriculture - p. 97 14. Chapter six. Crop health monitoring using geospatial methods and deep learning - p. 135 15. Chapter seven. Integrating climate data for agricultural resilience using geospatial approaches - p. 151 16. Chapter eight. Soil mapping and categorization using fusion of satellite imagery and machine learning - p. 167 17. Chapter nine. Geo-artificial intelligence for smart irrigation management systems - p. 195 18. Chapter Ten. Geospatial-based mapping and monitoring of pest and disease outbreaks utilizing machine learning - p. 213 19. Chapter eleven. Integration of geospatial technology and machine learning for livestock management - p. 257 20. Chapter Twelve. Machine learning and geospatial technology for agroforestry system mapping - p. 275 21. Chapter Thirteen. Geospatial and machine learning-based mapping and analysis for agricultural sustainability - p. 299 22. Chapter Fourteen. Deep learning and geospatial technology-based decision support systems for smart agricultural and irrigation applications - p. 331 23. Chapter fifteen. A case study on Lagrange polynomials and machine learning for yield prediction - p. 363 24. Chapter SIXTEEN. Leveraging deep learning ensembles for rice disease classification: a case study - p. 379 25. Chapter seventeen. A case study on optimizing crop classification with machine learning - p. 403 26. Chapter eighteen. Synthetic data generation using microwave modeling with efficient application of machine learning for bare land soil moisture retrieval: a case study - p. 427 27. Index - p. 453
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. Preface - p. xvii 8. Acknowledgments - p. xix 9. Chapter one. Overview of geospatial technology and machine learning in agriculture - p. 1 10. Chapter two. Spatial data acquisition methods for agricultural monitoring - p. 29 11. Chapter three. Machine learning techniques for crop identification and classification - p. 49 12. Chapter four. Predictive modeling and analysis of crop yield and productivity - p. 65 13. Chapter five. Integration of geospatial technology and machine learning for precision agriculture - p. 97 14. Chapter six. Crop health monitoring using geospatial methods and deep learning - p. 135 15. Chapter seven. Integrating climate data for agricultural resilience using geospatial approaches - p. 151 16. Chapter eight. Soil mapping and categorization using fusion of satellite imagery and machine learning - p. 167 17. Chapter nine. Geo-artificial intelligence for smart irrigation management systems - p. 195 18. Chapter Ten. Geospatial-based mapping and monitoring of pest and disease outbreaks utilizing machine learning - p. 213 19. Chapter eleven. Integration of geospatial technology and machine learning for livestock management - p. 257 20. Chapter Twelve. Machine learning and geospatial technology for agroforestry system mapping - p. 275 21. Chapter Thirteen. Geospatial and machine learning-based mapping and analysis for agricultural sustainability - p. 299 22. Chapter Fourteen. Deep learning and geospatial technology-based decision support systems for smart agricultural and irrigation applications - p. 331 23. Chapter fifteen. A case study on Lagrange polynomials and machine learning for yield prediction - p. 363 24. Chapter SIXTEEN. Leveraging deep learning ensembles for rice disease classification: a case study - p. 379 25. Chapter seventeen. A case study on optimizing crop classification with machine learning - p. 403 26. Chapter eighteen. Synthetic data generation using microwave modeling with efficient application of machine learning for bare land soil moisture retrieval: a case study - p. 427 27. Index - p. 453
