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Deep Learning and IoT: The Enabling Technologies Towards Smart Farming

Deep Learning and IoT: The Enabling Technologies Towards Smart Farming

Muhammad Suleman Memon, Pardeep Kumar, Azeem Ayaz Mirani, Mumtaz Qabulio, Irum Naz Sodhar
Copyright: © 2020 |Pages: 14
ISBN13: 9781799828037|ISBN10: 1799828034|ISBN13 Softcover: 9781799828044|EISBN13: 9781799828051
DOI: 10.4018/978-1-7998-2803-7.ch003
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MLA

Memon, Muhammad Suleman, et al. "Deep Learning and IoT: The Enabling Technologies Towards Smart Farming." Industrial Internet of Things and Cyber-Physical Systems: Transforming the Conventional to Digital, edited by Pardeep Kumar, et al., IGI Global, 2020, pp. 47-60. https://doi.org/10.4018/978-1-7998-2803-7.ch003

APA

Memon, M. S., Kumar, P., Mirani, A. A., Qabulio, M., & Sodhar, I. N. (2020). Deep Learning and IoT: The Enabling Technologies Towards Smart Farming. In P. Kumar, V. Ponnusamy, & V. Jain (Eds.), Industrial Internet of Things and Cyber-Physical Systems: Transforming the Conventional to Digital (pp. 47-60). IGI Global. https://doi.org/10.4018/978-1-7998-2803-7.ch003

Chicago

Memon, Muhammad Suleman, et al. "Deep Learning and IoT: The Enabling Technologies Towards Smart Farming." In Industrial Internet of Things and Cyber-Physical Systems: Transforming the Conventional to Digital, edited by Pardeep Kumar, Vasaki Ponnusamy, and Vishal Jain, 47-60. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2803-7.ch003

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Abstract

The agriculture sector plays a big part in the overall economy of any country. The population of the world is increasing day by day, which is also increasing the overall demand of the food. Due to various diseases and nature of the soil, it is difficult to meet the overall demand of the food. The agronomists and farmers also face many problems in the agriculture sector such as disease identification, knowing nature of the soil, pesticide management, etc. The old methods of managing crop do not help in an effective way in meeting the increasing demand for food. The modern methods such IOT, machine, deep learning, and image processing help to identify the crop health and to predict crop yield. This chapter generally discusses various state-of-the-art technologies and methods for predicting crop yield and disease identification.

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