Toward a Stage-wise Classification of Date Palm White Scale Disease using Features Extraction and Machine Learning Techniques

Date palms cultivation is a crucial factor in oasis agriculture. The latter plays an essential role in the socioeconomics of many countries, including Morocco. However, many diseases threaten this valuable tree causing economic and environmental damages. White Scale is one of the most harmful pests that negatively affect the quality of date fruits and reduce their yield. Therefore, early detection of infected plants is essential in any Pest Management System. In this study, we propose a framework based on feature extraction and Machine Learning techniques to automatically classify the degree of infestation by Date Palm White Scale Disease. Gray-Level Co-occurrence Matrix features and HSV Color Moments were extracted and combined, then fitted to a K-Nearest Neighbors classifier that outperformed an average accuracy of 96.90%. In addition, other metrics such as Precision, Recall, F1-score, and confusion matrix are calculated to obtain more details about the stage-wise classification performance of the proposed framework. Finally, we conducted a comparative analysis between the proposed framework and some state-of-the-art-based methods. As a result, the proposed approach surpasses the state-of-the-art models in terms of various performance metrics. It is anticipated that this application will help farmers and stakeholders across the country and worldwide on both a macro and micro scale.