Description

The date palm is a crop with significant socioeconomic and ecological importance in Morocco. Date palm is an important crop in terms of the number of trees and their distribution in the Egypt and worldwide. The disease is categorically identified so that proper process may also begin to prevent the losses in the yields. To explore two features (GLCM-based features and HSV Color Moments) to find the most effective for date palm classification. 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. With the recent advances in machine learning random forest and decision tree have been successfully used for data palm disease analysis. Finally, the system can estimate some performance metrics such as accuracy, precision, recall and f1-score effectively. The initial results of the experiments look promising in helping specialists and farmers detecting diseases in early stages.