Deep Learning for Sustainable Agriculture

Ramesh Chandra Poonia , Vijander Singh , Soumya Ranjan Nayak 지음

elsevier 2022년 01월 17일 출간

작품소개

책소개

Deep Learning for Sustainable Agriculture reviews the fundamental concepts of gathering, processing and analyzing different deep learning models, along with a review of methods that can be used in this direction. The book also covers novel deep learning techniques for effective agriculture data management with standards laid by international organizations in related fields. The book is centered around evolving novel intelligent/deep learning models to solve the mitigation of agriculture. There are several deep learning models known that are used for weather forecasting, plant disease detection, underground water detection, quality of soil, and many more issues in agriculture. Introduces the novel deep learning models needed to address sustainable solutions for issues related to agriculture Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture Offers perspectives for the design, development and commissioning of intelligent applications

목차

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

작가정보

저자(글) Ramesh Chandra Poonia

저자(글) Vijander Singh

저자(글) Soumya Ranjan Nayak