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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREMWP9885

Tomato Leaf Disease Prediction Using Deep Learning Techniques

Stream/Area:

-

Subject:

-

Paper ID:

IJSREMWP9885

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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

MC

M Charishma

BA

Beldona Anupama

SYA

Shaik Yasir Ahamed

KD

Kurakula Dinesh

SR

Swetha Rajagopal

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Abstract

Tomato cultivation is vital in India, but leaf diseases can reduce yields by 20–40%. Manual detection is time-consuming, labor-intensive, and costly. To address this, a synthesized dataset from agricultural fields was used to train three CNN models for early disease detection. A General CNN achieved 81% accuracy, MobileNet V2 chosen for its lightweight design and real-time suitability achieved 85.2%, while VGG Net reached 77%. Source code is available at https://github.com/Yasir727/Tomatoo-Leaf-Disease-Prediction.

KEYWORDS

Convolutional Neural Network (CNN) Early Disease Detection MobileNet V2 Precision Agriculture Tomato Leaf Disease

Cite This Article

M Charishma, Beldona Anupama, Shaik Yasir Ahamed, Kurakula Dinesh, Swetha Rajagopal, "Tomato Leaf Disease Prediction Using Deep Learning Techniques," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025.

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Publication Date

12/4/2025

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Double-Blind Peer Review

Review Rounds: 2

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5

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

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