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What is VRA

Chapter 11
Deep Learning Applications and Intelligent Decision Making in Engineering
Variable rate Application, adjustment of the amount of crop input such as seed, fertilizer, pesticides to match conditions in a field.
Published in Chapter:
Deep Learning Solutions for Agricultural and Farming Activities
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
DOI: 10.4018/978-1-7998-2108-3.ch011
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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