Deep Learning for Sustainable Agriculture

Deep Learning for Sustainable Agriculture 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. 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. Introduces novel deep learning models needed to address sustainable solutions for the issues related to agriculture by creating a sustainable solutionProvides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agricultureOffers perspectives for design, development, and commissioning of intelligent applications