article · Big Data Mining and Analytics
Date palm trees in oasis agriculture face severe threats from white scale disease caused by Parlatoria blanchardi, an insect pest that lowers fruit quality and can kill trees if severe infestations occur. Accurate identification of infection stages helps farmers determine whether chemical treatment is required. A machine learning framework was developed to classify date palm leaflet images into four stages: healthy, low, medium, and high infestation. The approach extracts eighty texture features alongside nine colour moments. Several algorithms, including support vector machines, k-nearest neighbours, random forest, and light gradient boosting machines, were evaluated across two feature configurations. A support vector machine trained on the combined texture and colour features achieved the highest accuracy at 98.29 per cent. This automated diagnostic approach offers a mechanism to support early detection and targeted interventions in date farming.
Date palms are vital to the economy and food supply in oasis communities, but insect pests risk destroying crops and trees. Determining the exact severity of white scale infestation allows farmers to decide when chemical treatments are strictly necessary. Automated image analysis provides an accurate, non-destructive way to monitor tree health, helping growers minimise yield losses and manage crop protection efficiently.
The framework could enable automated crop monitoring tools, mobile diagnostic applications, or precision spraying systems for date palm growers and agricultural advisory services. By accurately categorising infestation levels, it informs targeted chemical treatments. Because the methodology was tested on image datasets using classical and ensemble machine learning models, it currently represents early-stage to applied software research that requires field deployment and integration into practical farm management tools before real-world commercial use.
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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.
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DOI: 10.26599/bdma.2022.9020022
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