Description
Book SynopsisThis book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging
Table of ContentsMachine learning and deep learning in agriculture, Descriptive and predictive analytics of agricultural data using machine learning algorithms, Discrimination between weed and crop via image analysis using machine learning algorithm, Bio-inspired optimization algorithms for machine learning in agriculture applications, Agricultural modernization with forecasting stages and machine learning, Classification of segmented image using increased global contrast for Paddy plant disease, IOT in agriculture: Survey on technology, challenges and future scope, Role of IoT in sustainable farming, Smart farming: Crop models and decision support systems using IOT, Smart irrigation in farming using internet of things, Automation systems in agriculture via IOT, A complete automated solution for farm field and garden nurture using internet of things, Machine intelligence techniques for agricultural production: Case study with tomato leaf disease detection, Clock signal and its attribute for agriculture.
