ORIGINAL ARTICLE
Sweet pepper foliar diseases quantification and identification using an image analysis tool
1
Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies
St. Augustine Campus, Trinidad and Tobago
2
Department of Life Science, Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus,
Trinidad and Tobago
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: 2024-04-25
Acceptance date: 2024-07-25
Online publication date: 2025-03-25
Corresponding author
Vijayanadh Rajamanickam
Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies St. Augustine Campus, Trinidad and Tobago
Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies St. Augustine Campus, Trinidad and Tobago
HIGHLIGHTS
- Images captured by smartphones were used in digital tools.
- The farmer or user can get the results very quickly with the use of digital tools.
- Digital tools for quantifying & identifying diseases of sweet pepper foliar diseases
- Thresholding and texture-based disease detection
- GLCM Texture features have been extracted and classified by various ML classifiers
KEYWORDS
TOPICS
ABSTRACT
Quantification and assessment of disease symptoms are important elements of plant disease
management systems and are required to assist with making decisions on the choice
of protective agents to be applied to crops or for screening plant genotypes for the development
of resistant varieties. Traditional methods of identifying and quantifying disease
severity are cumbersome, involving visual assessment tools or scales, and rating of plants at
a point in time. Visual assessment is prone to human bias and error, thereby reducing the
efficiency and accuracy of this method. In this study, we developed a smartphone camera-
-based image recording, processing, and assessment tool for measurement of symptoms of
early and late blight, and bacterial leaf spot diseases in sweet pepper caused by Alternaria
solani, Phytophthora infestans, and Xanthomonas campestris pv. vesicatoria, respectively.
Sweet pepper or bell pepper is a major vegetable crop grown in the Caribbean region, but
production is severely affected by plant diseases, most important of which include foliar
infections by fungi and bacteria that cause major losses in fruit yield. This research utilized
smartphone captured images of leaf specimens for severity measurement and classification
of diseases. The steps involved were color space conversions, detection of leaf area by Otsu’s
method, and thresholding for foliar diseased area detection and quantification. Gray-Level
Co-occurrence Matrix (GLCM) extracted the texture features from the diseased area of
leaves. These features are trained and classified by various machine learning classifiers including
trees, rule-based and Bayes models. Application of decision trees and rule-based
classifier models achieved 98% accuracy individually, while Bayes model achieved 86% accuracy.
The image input into the above classifier models resulted in fast and accurate identification
of the diseases by matching the features of trained images of disease symptoms.
This method could work well for leaves collected from field-grown plants as well as from
inoculated greenhouse plants.
ACKNOWLEDGEMENTS
We wish to thank the Campus Research and Publication
Fund Committee, The University of the West Indies,
St. Augustine Campus, Trinidad and Tobago for
funding this study and the farmers of Macoya, Orange
Grove and Aranguez for their assistance in the collection
of plant samples.
RESPONSIBLE EDITOR
Piotr Iwaniuk
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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