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.