XGBoost Ensemble Algorithm for Classifying Tomato Leaf Diseases Based on Texture Descriptors

This article presents a simple and understandable approach to the automatic assessment of the severity of late blight on tomato leaves. We collect our own dataset of 5245 RGB images of healthy and diseased tomato leaves and determine five ordinal classes: healthy (0%) and four infection levels (0.1–10%, 11–25%, 26–50%, and ≥51% of the affected area). Each image is segmented using the global definition of the Otsu threshold, followed by morphological purification, after which seven textural and geometric characteristics are extracted from the contours of the lesion: contrast, number of contours, average and standard deviation of the contour area, average and standard deviation of the contour perimeter, and average area-to-perimeter ratio. All characteristics are normalized and used as input data for the XGBoost classifier. The dataset is randomly split into 80% training and 20% test images, resulting in an independent test set of 1049 images. In this test set, the proposed model provides an overall accuracy of 0.93 and an F1 macro score of 0.93 points, while for each F1 class, it varies from 0.90 to 0.97. The confusion matrix shows a stable difference between neighboring severity levels, while the analysis of the importance of the features confirms the relevance of contour descriptors for characterizing the size and shape of the lesion. This method only runs on a central processor, requires a small amount of memory, and outputs interpretable output data, making it suitable for use in greenhouses and farms with limited computing resources. We also discuss the limitations associated with the boundaries between neighboring classes and the potential shift in the subject area, and we outline directions for expanding the approach to multi-sheet scenes and explicit ordinal loss functions.

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