The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques…mehr
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offeredby an advanced mathematical evolutionary algorithm.
Dr. Vijander Singh is working as Assistant Professor, Department of Computer Science and Engineering, Manipal University Jaipur, India. He received Ph.D. degree from Banasthali University, Banasthali, India, in April 2017. He has published 25 research papers in indexed journals and several book chapters for international publishers. He authored two books and handled/handling journals of international repute such as Taylor & Francis, Taru Publication, IGI Global, Inderscienc, etc. as guest editor. He is an associate editor of TARU Journal of Sustainable Technologies and Computing (TJSTC). He has organized several International Conferences, FDPs, and Workshops as a core team member of the organizing committee. His research area includes Machine Learning, Deep Learning, Precision Agriculture, and Networking.
Inhaltsangabe
1. Smart agriculture: Technological advancements on agriculture: A systematical review 2. A systematic review of artificial intelligence in agriculture 3. Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial vehicles through classification and optimization process of machine learning with convolution neural network 4. Design and implementation of a crop recommendation system using nature-inspired intelligence for Rajasthan, India 5. Artificial intelligent-based water and soil management 6. Machine learning for soil moisture assessment 7. Automated real-time forecasting of agriculture using chlorophyll content and its impact on climate change 8. Transformations of urban agroecology landscape in territory transition 9. WeedNet: A deep neural net for weed identification 10. Sensors make sense: Functional genomics, deep learning, and agriculture 11. Crop management: Wheat yield prediction and disease detection using an intelligent predictive algorithms and metrological parameters 12. Sugarcane leaf disease detection through deep learning 13. Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture 14. Artificial intelligence-based detection and counting of olive fruit flies: A comprehensive survey
1. Smart agriculture: Technological advancements on agriculture: A systematical review 2. A systematic review of artificial intelligence in agriculture 3. Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial vehicles through classification and optimization process of machine learning with convolution neural network 4. Design and implementation of a crop recommendation system using nature-inspired intelligence for Rajasthan, India 5. Artificial intelligent-based water and soil management 6. Machine learning for soil moisture assessment 7. Automated real-time forecasting of agriculture using chlorophyll content and its impact on climate change 8. Transformations of urban agroecology landscape in territory transition 9. WeedNet: A deep neural net for weed identification 10. Sensors make sense: Functional genomics, deep learning, and agriculture 11. Crop management: Wheat yield prediction and disease detection using an intelligent predictive algorithms and metrological parameters 12. Sugarcane leaf disease detection through deep learning 13. Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture 14. Artificial intelligence-based detection and counting of olive fruit flies: A comprehensive survey
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