About the Book:
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.
Table of Contents
Chapter 1: Smart agriculture: Technological advancements on agriculture
Chapter 2: A systematic review of artificial intelligence in agriculture
Chapter 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
Chapter 4: Design and implementation of a crop recommendation system using nature-inspired intelligence for Rajasthan, India
Chapter 5: Artificial intelligent-based water and soil management
Chapter 6: Machine learning for soil moisture assessment
Chapter 7: Automated real-time forecasting of agriculture using chlorophyll content and its impact on climate change
Chapter 8: Transformations of urban agroecology landscape in territory transition
Chapter 9: WeedNet: A deep neural net for weed identification
Chapter 10: Sensors make sense: Functional genomics, deep learning, and agriculture
Chapter 11: Crop management: Wheat yield prediction and disease detection using an intelligent predictive algorithms and metrological parameters
Chapter 12: Sugarcane leaf disease detection through deep learning
Chapter 13: Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture
Chapter 14: Artificial intelligence-based detection and counting of olive fruit flies: A comprehensive survey
