Journal of Network and Information Security

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A Review on Crop Disease Detection using Deep Learning


Affiliations
1 Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, India
 

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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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  • A Review on Crop Disease Detection using Deep Learning

Abstract Views: 302  |  PDF Views: 121

Authors

Dilli Charan Sai
Seshachala Degree & P.G. College, Puttur, Andhra Pradesh, India

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.

References