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.

The Internet of Things (IoT) is currently reflected in the increase in the number of connected objects, that is, devices with their own identity and computing and communication capacities. IoT is recognized as one of the most critical areas for future technologies, gaining worldwide attention. It applies to many areas, where it has achieved success, such as healthcare, where a patient is monitored using nodes and lightweight sensors. However, the powerful functions of IoT in the medical field are based on communication, analysis, processing, and management of data autonomously without any manual intervention, which presents many difficulties, such as energy consumption. However, these issues significantly slow down the development and rapid deployment of this technology. The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading. The distance between nodes is one of the factors influencing energy consumption. In this article, we have proposed direct communication between nodes to avoid collision domains, which will help reduce data retransmission. The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.