04626cam a22006018i 4500 9781003138884 FlBoTFG 20260210180745.0 m d u cr ||||||||||| 200811s2021 flu o 000 0 eng OCoLC-P eng rda OCoLC-P 9781003138884 (ebook) 1003138888 9780367535803 (hardback) 9781000327892 (ePub ebook) 1000327892 9781000327878 (PDF ebook) 1000327876 9781000327885 (Mobipocket ebook) 1000327884 9780367687687 10.1201/b22627 doi (OCoLC)1184123376 (OCoLC-P)1184123376 S494.5.D3 SCI 011000 bisacsh TEC 003000 bisacsh TEC 046000 bisacsh TVB bicssc 338.10285 23 Smart agriculture : emerging pedagogies of deep learning, machine learning and Internet of Things / edited by Govind Singh Patel, LPU Phagwara, India, Amrita Rai, UPTU, India, Nripendra Narayan Das, Manipal University Jaipur, India, R.P. Singh, Haramaya University, Diredawa, Ethiopia. First edition. Boca Raton : CRC Press/Balkema/Taylor & Francis Group, [2021] 1 online resource text txt rdacontent computer n rdamedia online resource nc rdacarrier "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"-- Provided by publisher. 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. OCLC-licensed vendor bibliographic record. Artificial intelligence Agricultural applications. Agricultural innovations. Machine learning. 1623 Internet of things. Big data. SCIENCE / Life Sciences / Botany bisacsh TECHNOLOGY / Agriculture / General bisacsh TECHNOLOGY / Machinery bisacsh Patel, Govind Singh, editor. Taylor & Francis https://www.taylorfrancis.com/books/9781003138884 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 90332 90331