Academic Press Deep Learning for Sustainable Agriculture (Cognitive Data Science in Sustainable Computing)

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. Review Reviews the fundamental concepts of gathering, processing, and analyzing different deep learning models From the Back Cover <p><i>Deep Learning for Sustainable Agriculture </i>reviews the fundamental concepts of gathering, processing, and analyzing different deep learning models, followed by a review of methods that can be used in this direction. The book also covers the novel Deep Learning techniques for effective agriculture data management, with the standards laid by international organizations in related fields.</p> <p>The book is centered around the evolving novel intelligent/deep learning models to solve the mitigation of agriculture. There are several deep learning models know among which few are used for weather forecasting, plant disease detection, underground water detection, quality of soil, and many more issues in agriculture. This book provides such models developed in deep learning and their applications at a single platform. Traditional methods of agriculture are major reasons behind inefficient utilization & wastage of the resources. Utilizing the deep learning methods in the field of agriculture will increase the efficiency of the farmers and use the resources in an optimized way.</p> About the Author 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. 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