Performance evaluation of ResNet model for classification of tomato plant disease

Sachin Kumar; Saurabh Pal; Vijendra Pratap Singh; Priya Jaiswal

doi:10.1515/em-2021-0044

Article

Performance evaluation of ResNet model for classification of tomato plant disease

Sachin Kumar , Saurabh Pal , Vijendra Pratap Singh and Priya Jaiswal

Published/Copyright: January 12, 2023

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From the journal Epidemiologic Methods Volume 12 Issue 1

Abstract

Objectives

The plant tomato (Solanum Lycopersicum) is vastly infected by various diseases. Exact diagnosis on time contributes a significant job to the good production of tomato crops. The key objective of this article is to recognize the infection in tomato leaves with better accuracy and in less time.

Methods

Nowadays deep convolutional neural networks have attained surprising outcomes in several applications, together with the categorization of tomato leaves infected with several diseases. Our work is based on deep CNN with different residual networks. Finally; we have performed tomato leaves disease classification by using pre-trained deep CNN with the residual network using MATLAB available on the cloud.

Results

We have used a dataset of tomato leaves for the experiments which contain six different types of diseases with one healthy tomato leaf class. We have collected 6,594 tomato leaves dataset from Plant Village and we did not collect actual tomato leaves for testing. The outcome obtained by ResNet-50 shows a significant result with 96.35% accuracy for 50% training and 50% testing data and if we focus on time consumption for the outcome then ResNet-18 consumes 12.46 min for 70% training and 30% testing.

Conclusions

After observation of several outcomes, we have concluded that ResNet-50 shows a better accuracy for 50% training and 50% testing of data and ResNet-18 shows better efficiency for 70% training and 30% testing of data for the same dataset on the cloud.

Keywords: classification; convolutional neural networks; ResNet-101; ResNet-18; ResNet-50; tomato plant diseases

Corresponding author: Sachin Kumar, PhD Research Scholar, Department of Computer Application, V.B.S P.U, Jaunpur, U.P, India, E-mail: jaiswalsachin009@gmail.com

Acknowledgments

All authors are acknowledged for this research paper.

Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. We declare that, there is no financial interests/personal relationship which may be considered as potential competing interests.
Informed consent: Not Applicable.
Ethical approval: Not Applicable.

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Received: 2021-12-29

Accepted: 2022-12-06

Published Online: 2023-01-12

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Articles in the same Issue

https://doi.org/10.1515/em-2021-0044

Keywords for this article

classification; convolutional neural networks; ResNet-101; ResNet-18; ResNet-50; tomato plant diseases