Deep Learning for Sustainable Agriculture (Paperback)

Part 1: Sustainable Agriculture 1. Agriculture: Need of hour 2. Issues and challenges in agriculture 3. Traditional and modern agriculture 4. Approaches/Technologies used for sustainable agriculture

Part 2. Introduction to Neural Network and deep learning as subset of AI 5. Definition of AI 6. Fundamentals of AI 7. Machine learning versus Deep learning/Neural Network 8. Image data, dataset and their importance in deep learning

Part 3 Deep Learning Models in sustainable agriculture 9. Image Fundamentals and classification 10. Feature extraction techniques 11. Deep Learning Models 12. Optimization Methods and Regularization 13. Efficient Image parsing in agriculture

Part 4: Multi Instance and multistage deep learning for agriculture image recognition 14. Introduction 15. Related Work 16. Methodology 17. Results and Analysis

Part 5: Scalable high-performance image registration framework by deep feature representation learning 18. Introduction 19. Proposed method 20. Results and Discussion

Part 6. Deep learning for image-based plant disease detection 21. Common diseases their characteristics 22. Applicable models for disease detection 23. Computational approaches for feature selection 24. Future Scope of sustainable agriculture

Part 7 Convolution Neural Network (CNN) for robust and real time image registration 25. Problem formulation 26. Image Model 27. Regression Strategy 28. Feature Extraction 29. CNN 30. Results and discussion

Part 8: Natural language processing for large scale agriculture image analysis using deep learning 31. Fundamental of NLP 32. Neural language models 33. Prediction of common disease in the common crops 34. Results and discussion

Part 9: Case Studies: Common crops and corresponding disease prediction 35. Case study 1 36. Case Study 2 37. Case Study 3