ORIGINAL ARTICLE
Extreme gradient boosting driven intelligent system for tomato leaf disease identification
1
Department of Computing and Information Technology, The University of the West Indies, St Augustine, 330912, St Augustine, Trinidad and Tobago
2
Department of Life Sciences, The University of the West Indies, St Augustine, 330912, St Augustine, Trinidad and Tobago
These authors had equal contribution to this work
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2025-05-23
Acceptance date: 2025-09-10
Online publication date: 2026-08-18
Corresponding author
Vijayanandh Rajamanickam
Department of Computing and Information Technology, The University of the West Indies, St Augustine, 330912, St Augustine, Trinidad and Tobago
Department of Computing and Information Technology, The University of the West Indies, St Augustine, 330912, St Augustine, Trinidad and Tobago
Journal of Plant Protection Research 2026;66(3):398-408
HIGHLIGHTS
- Focus on major regional diseases prevalent in the Caribbean
- Applied advanced image preprocessing to improve image quality
- Extracted comprehensive features including color histograms, LBP and GLCM
- Trained 11,979 images from PlantVillage dataset and achieved an accuracy of 97.20%
- Effective and accurate disease identification achieved with XGBoost
KEYWORDS
TOPICS
ABSTRACT
Tomato (Solanum lycopersicum) is an important vegetable crop which is susceptible to
multiple diseases that can impact yield and produce quality. The current research aimed
to classify major tomato diseases in the Caribbean region through a developed intelligent
system. The system was based on six key diseases including bacterial spot, early blight, late
blight, septoria leaf spot, yellow leaf curl virus, and mosaic virus. To improve the identification
accuracy, all images were first resized and then segmented using the GrabCut
method to isolate the leaf regions. The segmented images were subsequently converted to
Hue, Saturation, and Value (HSV) color space and grayscale before feature extraction was
performed. A total of 96 features were extracted, including color histograms, Local Binary
Pattern (LBP), and Gray-Level Co-Occurrence Matrix (GLCM). The Extreme Gradient
Boosting (XGBoost) algorithm was employed for disease identification. This study used
a dataset of 11,979 images from the PlantVillage collection, including both healthy and
diseased samples. The developed model achieved a disease identification accuracy of
97.20% while remaining computationally efficient.
ACKNOWLEDGEMENTS
We thank the Campus Research and Publication Fund
Committee, The University of the West Indies, St. Augustine
Campus, Trinidad and Tobago for funding this
study.
RESPONSIBLE EDITOR
Assunta Bertaccini
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
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| eISSN: | 1899-007X |
| ISSN: | 1427-4345 |
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