Deep Learning Solutions for Agricultural and Farming Activities

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Author(s): Asha Gowda Karegowda (Siddaganga Institute of Technology, India), Devika G. (Government Engineering College, India)and Geetha M. (Bharat Institute of Engineering and Technology, India)
Copyright: 2021
Pages: 32
Source title: Deep Learning Applications and Intelligent Decision Making in Engineering
Source Author(s)/Editor(s): Karthikrajan Senthilnathan (Revoltaxe India Pvt Ltd, Chennai, India), Balamurugan Shanmugam (Quants IS & CS, India), Dinesh Goyal (Poornima Institute of Engineering and Technology, India), Iyswarya Annapoorani (VIT University, India)and Ravi Samikannu (Botswana International University of Science and Technology, Botswana)
DOI: 10.4018/978-1-7998-2108-3.ch011

Keywords: Artificial Intelligence / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The continuously growing population throughout globe demands an ample food supply, which is one of foremost challenge of smart agriculture. Timely and precise identification of weeds, insects, and diseases in plants are necessary for increased crop yield to satisfy demand for sufficient food supply. With fewer experts in this field, there is a need to develop an automated system for predicting yield, detection of weeds, insects, and diseases in plants. In addition to plants, livestock such as cattle, pigs, and chickens also contribute as major food. Hence, livestock demands precision methods for reducing the mortality rate of livestock by identifying diseases in livestock. Deep learning is one of the upcoming technologies that when combined with image processing promises smart agriculture to be a reality. Various applications of DL for smart agriculture are covered.

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