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Deep Learning for Sustainable Agriculture. Cognitive Data Science in Sustainable Computing

  • ID: 5390314
  • Book
  • January 2022
  • Elsevier Science and Technology

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 offered by an advanced mathematical evolutionary algorithm.

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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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Ramesh Chandra Poonia Associate Professor
Department of Computer Science,CHRIST (Deemed to be University), Bangalore, Karnataka, India. Dr. Ramesh Chandra Poonia is an Associate Professor at the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India. Recently completed his Postdoctoral Fellowship from CPS Lab, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Ålesund, Norway. He has received his Ph.D. degree in Computer Science from Banasthali University, Banasthali, India in July 2013. His research interests are Cyber-Physical Systems, Network Protocol Evaluation and Artificial Intelligence. He is Chief Editor of TARU Journal of Sustainable Technologies and Computing (TJSTC) and Associate Editor of the Journal of Sustainable Computing: Informatics and Systems, Elsevier. He also serves in the editorial boards of a few international journals. He is main author and co-author of 06 books and an editor of more than 25 special issue of journals and books including Springer, CRC Press - Taylor and Francis, IGI Global and Elsevier, edited books and Springer conference proceedings and has authored/co-authored over 65 research publications in peer-reviewed reputed journals, book chapters and conference proceedings. Vijander Singh Associate Professor, Department of Computer Science and Engineering, Manipal University Jaipur, India. 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. Soumya Ranjan Nayak Assistant Professor, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India. Soumya Ranjan Nayak is an assistant professor at the Amity School of Engineering and Technology, Amity University, Noida, India. He received his PhD degree in computer science and engineering under the MHRD Govt. of India. He has published over 80 articles in peer-reviewed journals and conferences of international repute like Elsevier, Springer, World Scientific, IOS Press, Taylor & Francis, Inderscience, and IGI Global. His current research interests include medical image analysis and classification, machine learning, deep learning, pattern recognition, fractal graphics, and computer vision. He serves as a reviewer for many peer-reviewed journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Access, Applied Mathematics and Computation, Journal of Applied Remote Sensing, Mathematical Problems in Engineering, International Journal of Light and Electron optics, Journal of Intelligent and Fuzzy Systems, Future Generation Computer Systems, and Pattern Recognition Letters.
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