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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM45623

Detection of Potato Leaf Disease using CNN for High-Accuracy Diagnosis

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM45623

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem45623

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

ST

Shivam Tripathi

PD

Priyanshu Dimri

SK

Sandeep Kumar

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Abstract

Crop production rates are a major problem for India, an agricultural nation. Low output makes the crops expensive and causes hunger for people who cannot even buy basic necessities like potatoes. Deep learning technologies can make farmers’ lives easier and more productive by increasing agricultural yield and reducing crop disease infections. A subfield of artificial intelligence called deep neural networks helps farmers detect plant diseases early on, giving them enough time to take action before it’s too late. Convolutional Neural Networks (CNN) are better at detecting leaf illnesses, according to this paper’s evaluation of multiple studies. Additionally, CNNs have the highest accuracy in identifying diseases and are able to identify even minute patterns that are invisible to the naked eye.

KEYWORDS

Crop Production Deep Learning Plant Disease Detection Convolutional Neural Networks (CNN) Agricultural Yield Artificial Intelligence in Agriculture

Cite This Article

Shivam Tripathi, Priyanshu Dimri, Sandeep Kumar, "Detection of Potato Leaf Disease using CNN for High-Accuracy Diagnosis," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/ijsrem45623

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Publication Date

4/23/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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