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A Review on Crop Disease Detection using Deep Learning

Journal of Network and Information Security

Volume 9 Issue 1

Published: 2021
Author(s) Name: Dilli Charan Sai | Author(s) Affiliation: Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, India.
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Abstract

Lately, extreme atmosphere changes and absence of invulnerability in yields has caused a significant increment in the development of harvest infections. This causes enormous scale devastation of harvests, diminishes development and inevitably prompts money related loss of ranchers. Because of quick development in an assortment of maladies and sufficient information of rancher, distinguishing proof and treatment of the sickness has turned into a noteworthy test. The leaves have surface and visual likenesses which characteristics for an ID of illness type. Henceforth, PC vision utilized with profound learning gives the best approach to tackle this issue. This paper proposes a profound learning-based model which is prepared utilizing open dataset containing pictures of solid and infected harvest leaves. The model serves its target by arranging pictures of leaves into infected classification dependent on the example of imperfection.

Keywords: Crop disease, Deep learning, Image classification, InceptionV3, MobileNet, Transfer learning.

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