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220317 ||| eng |
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|a 1000327884
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|a 9781000327892
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|a 9781000327885
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|a 1003138888
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|a 9781000327878
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|a 9781003138884
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|a 1000327876
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|a 1000327892
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4 |
|a S494.5.D3
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| 100 |
1 |
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|a Patel, Govind Singh
|e editor
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| 245 |
0 |
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|a Smart agriculture
|b emerging pedagogies of deep learning, machine learning and Internet of Things
|c edited by Govind Singh Patel, associate editors, Amrita Rai, Nripendra Narayan Das, R.P. Singh
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| 250 |
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|a First edition
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| 260 |
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|a Leiden, The Netherlands
|b CRC Press/Balkema
|c 2021
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| 300 |
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|a vi, 211 pages
|b illustrations
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| 505 |
0 |
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|a Machine learning and deep learning in agriculture / Sumit Koul -- Descriptive and predictive analytics of agricultural data using machine learning algorithms / Mrs. R. Suguna, Dr. R. Uma Rani -- Discrimination between weed and crop via image analysis using machine learning algorithm / P Amsin, Dr. R. Ma Rani -- Bio-inspired optimization algorithms for machine learning in agriculture applications / P.R. MahiDar -- Agricultural modernization with forecasting stages and machine learning / A.K. Awasthi and Arun Kumar Garov -- Classification of segmented image using increased global contrast for paddy plant disease / Md Abdul Muqueem, G. Raju and Govind Singh Patel -- IoT in agriculture : Survey on technology, challenges and future scope / Seema Nayak -- Role of IoT in sustainable farming / Rajasekhar Manda and P.Rajesh Kumar -- Smart farming : Crop models and decision support systems using IoT / Kanwaljeet Singh and Amandeep Kaur -- Smart irrigation in farming using internet of things / Devesh Kumar Srivastava and Priyanka Nair -- Automation systems in agriculture via IoT / Shylaja Vinay Kumar Karatangi, Reshu Agarwal and Manisha Pant -- A complete automated solution for farm field and garden nurturing using Internet of Things / S. Siva Kumar, A. M. Senthil Kumar, T. Rajesh Kumar, N. Sree Ram, V. Krishna Reddy and P. Sivakumar -- Machine intelligence techniques for agricultural production : Case study with tomato leaf disease detection / Mihir Narayan Mohanty -- Clock signal and its attribute for agriculture / Abhishek Kumar
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| 505 |
0 |
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|a Includes bibliographical references
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| 653 |
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|a SCIENCE / Life Sciences / Botany / bisacsh
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| 653 |
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|a Big data / fast
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| 653 |
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|a Intelligence artificielle / Applications agricoles
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| 653 |
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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| 653 |
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|a Agricultural innovations / http://id.loc.gov/authorities/subjects/sh85002334
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| 653 |
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|a Données volumineuses
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Agricultural innovations / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Internet of things / fast
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| 653 |
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|a TECHNOLOGY / Agriculture / General / bisacsh
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| 653 |
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|a Agriculture / Innovations
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| 653 |
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|a Internet des objets
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a TECHNOLOGY / Machinery / bisacsh
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| 653 |
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|a Internet of things / http://id.loc.gov/authorities/subjects/sh2013000266
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| 653 |
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|a Artificial intelligence / Agricultural applications / http://id.loc.gov/authorities/subjects/sh95006971
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| 653 |
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|a Artificial intelligence / Agricultural applications / fast
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 015 |
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|a GBC0J4631
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| 024 |
8 |
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|a 10.1201/b22627
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| 776 |
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|z 9781000327892
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| 776 |
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|z 1000327892
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| 776 |
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|z 9780367687687
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| 776 |
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|z 9781003138884
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| 776 |
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|z 1003138888
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| 776 |
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| 776 |
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|z 1000327884
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| 776 |
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| 776 |
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|z 9780367535803
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| 776 |
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|z 1000327876
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781000327892/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 338.10285
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| 082 |
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|a 500
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| 520 |
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|a "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"--
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