The apple industry suffers significant economic losses each year due to diseases and pests. Farmers face difficulties in identifying different apple diseases because the symptoms they produce can be very similar and may occur simultaneously. Traditional methods of disease identification, such as visual inspection, can be time-consuming and inaccurate, especially in the early stages of disease development. Deep learning has emerged as a promising new approach for apple disease classification and identification. Deep learning models can be trained on large datasets of images of healthy and diseased apple leaves and fruits, and can then be used to classify new images with high accuracy. This research paper aims to address this challenge by presenting a timely and accurate method for detecting and identifying apple diseases. The study begins with the creation of a comprehensive dataset through data collection and labeling. The proposed CNN model achieves promising results with good accuracy. These results validate the effectiveness of the proposed method in classifying various types of apple diseases. The research outcome offers a practical tool that can be utilized by farmers to enhance disease detection and identification, potentially mitigating the economic losses suffered by the apple industry.

CNN, Deep Learning, Apple Diseases, Scab.