This 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 agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

chapter Chapter 1|19 pages

Machine learning and deep learning in agriculture

chapter Chapter 7|8 pages

IoT in agriculture

Survey on technology, challenges and future scope

chapter Chapter 9|15 pages

Smart farming

Crop models and decision support systems using IoT

chapter Chapter 13|23 pages

Machine intelligence techniques for agricultural production

Case study with tomato leaf disease detection

chapter Chapter 14|14 pages

Clock signal and its attribute for agriculture