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Open Access

A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease

STI Laboratory, IDMS, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia 52000, Morocco.
IA Laboratory, Department of Computer Science, Faculty of Sciences, Moulay Ismail University of Meknes, Meknes 50070, Morocco.
RO.AL&I Group, Computer Sciences Department, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia 52000, Morocco.
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Abstract

Date palm production is critical to oasis agriculture, owing to its economic importance and nutritional advantages. Numerous diseases endanger this precious tree, putting a strain on the economy and environment. White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates. When an infestation reaches a specific degree, it might result in the tree’s death. To counter this threat, precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary. This decision is crucial for farmers who wish to minimize yield losses while preserving production quality. For this purpose, we propose a feature extraction and machine learning (ML) technique based framework for classifying the stages of infestation by white scale disease (WSD) in date palm trees by investigating their leaflets images. 80 gray level co-occurrence matrix (GLCM) texture features and 9 hue, saturation, and value (HSV) color moments features are extracted from both grayscale and color images of the used dataset. To classify the WSD into its four classes (healthy, low infestation degree, medium infestation degree, and high infestation degree), two types of ML algorithms were tested; classical machine learning methods, namely, support vector machine (SVM) and k-nearest neighbors (KNN), and ensemble learning methods such as random forest (RF) and light gradient boosting machine (LightGBM). The ML models were trained and evaluated using two datasets: the first is composed of the extracted GLCM features only, and the second combines GLCM and HSV descriptors. The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%. The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease (DPWSD) and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.

Keywords

precision agriculturemachine learningensemble learningfeature extractiondate palmdiseases

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Big Data Mining and Analytics
Volume 6 Issue 3,
September 2023
Pages 263-272
DOI: 10.26599/BDMA.2022.9020022
Cite this article:
Hessane A, El Youssefi A, Farhaoui Y, et al. A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease. Big Data Mining and Analytics, 2023, 6(3): 263-272. https://doi.org/10.26599/BDMA.2022.9020022

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Received: 18 June 2022
Accepted: 06 July 2022
Published: 07 April 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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