Abstract
The palm tree is considered one of the most durable trees, and it occupies an advanced position as one of the most famous and most important trees that are planted in different regions around the world, which enter into many uses and have a number of benefits. In the recent years, date palms have been exposed to a large number of diseases. These diseases differ in their symptoms and causes, and sometimes overlap, making the diagnosing process with the naked eye difficult, even by an expert in this field. This paper proposes a CNN-model to detect and classify four common diseases threatening palms today, Bacterial leaf blight, Brown spots, Leaf smut, white scale in addition to healthy leaves. The proposed CNN structure includes four convolutional layers for feature extraction followed by a fully connected layer for classification. For performance evaluation, we investigate the performance of the proposed model and compare to other CNN-structures, VGG-16 and MobileNet, using four evaluation metrics: Accuracy, Precision, Recall and F1 Score. Our proposed model achieves 99.10% accuracy rate while VGG-16 and MobileNet achieve 99.35% and 99.56% accuracy rates, respectively. In general, the performance of our model and other models are very close with a minor advantage to MobileNet over others. In contrast, our model characterized by simplicity and shows low computational training time comparing to others.