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. 2023 Oct 31;9(11):e21697.
doi: 10.1016/j.heliyon.2023.e21697. eCollection 2023 Nov.

A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features

Chimango Nyasulu  1   2 , Awa Diattara  1 , Assitan Traore  3 , Cheikh Ba  1 , Papa Madiallacké Diedhiou  4 , Yakhya Sy  4 , Hind Raki  2   5 , Diego Hernán Peluffo-Ordóñez  2   5   6
Affiliations

A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features

Chimango Nyasulu et al. Heliyon. .

Abstract

Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. Small differences between symptoms of these diseases make it difficult to use the naked eye to obtain better results in detecting and distinguishing these diseases. The Artificial Neural Network outperformed other classifiers with an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. Generally, the models confused samples originally belonging to Helminthosporium with Curvularia. The extracted texture features show great potential to classify the different tomato leaf fungal diseases. The results of this study show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in the establishment of tomato leaf disease classification systems and can facilitate the implementation of preventive measures by farmers, resulting in enhanced yield quality and quantity.

Keywords: Classification; Gray level co-occurrence matrix; Image processing; Machine learning; Tomato disease.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
Sample images for each class ((a) Alternaria, (b) Healthy, (c) Curvularia, (d) Helminthosporium, (e) Lasiodiplodi).
Figure 2
Figure 2
Summary of the steps followed in the study implementation.
Figure 3
Figure 3
ANN architecture for tomato fungal disease classification.
Figure 4
Figure 4
ANN confusion matrix summarizing correct and incorrect classification.
Figure 5
Figure 5
KNN confusion matrix summarizing correct and incorrect classification.
Figure 6
Figure 6
RF confusion matrix summarizing correct and incorrect classification.
Figure 7
Figure 7
SVM confusion matrix summarizing correct and incorrect classification.
Figure 8
Figure 8
ANN Receiver Characteristic Operator curve.
Figure 9
Figure 9
KNN Receiver Characteristic Operator curve.
Figure 10
Figure 10
RF Receiver Characteristic Operator curve.
Figure 11
Figure 11
SVM Receiver Characteristic Operator curve.
Figure 12
Figure 12
ANN model accuracy vs sample size.
See this image and copyright information in PMC

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