Prediction of Disease in Apple Leaf Using Convolution Neural Networks

Abstract

Abstract: Apple tree diseases can significantly impact fruit quality and yield, making early detection and intervention crucial for orchard management. To avoid the impact on apple production a novel approach is required for the early and accurate prediction of apple leaf diseases through Convolutional Neural Networks (CNNs). Our proposed system contains a comprehensive dataset of high-resolution pictures of apple leaves exhibiting various disease symptoms, including common issues like apple scab, apple rust, and powdery mildew. The dataset was carefully annotated to train and validate the CNN model effectively. The proposed CNN model makes use of its ability to automatically learn applicable features from images, making it highly suitable for the task of disease prediction in apple leaves. This model contributes to the field of precision agriculture by offering a cost-effective and efficient tool for apple disease detection.