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.
Table of Contents
Machine 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.
Dr. Govind Singh Patel has a PhD in Electronics and Communication Engineering from Thapar University, Patiala, India. He is working as a Professor in Lovely Professional University, Jalandhar, PB, India, and has published more than 45 papers in National and International Journals. He is a reviewer of many international journals like Springer, JCTN and more.
Dr. Amrita Rai received PhD in Electronics and Communication Engineering from Thapar University, Patiala, India. She is working as Associate Professor in UPTU, India. She has published more than 40 papers in National and International Journals.
Dr. Nripendra Narayan Das received PhD in Computer Science Engineering from Gautam Budda University, UP, India. He is working as Associate Professor in Manipal University, Jaipur, India. He has published more than 30 papers in National and International Journals.
Dr. R. P. Singh is working as Assistant Professor in School of Electrical and Computer Engineering, Haramaya Institute of Technology, Haramaya University, Diredawa, Ethopia, Africa. He has published more than 25 papers in National and International Journals.