Classification of Palm Trees Diseases using Convolution Neural Network

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