In many parts of India, apple trees are among the most popular crops. Large amounts of apples are exported annually, which has a major positive impact on the country's economy. However, a number of diseases in apple trees are common.They indicate a significant risk to apple production and lead to significant financial losses for producers. These diseases mostly affect the leaves of apple plants. In a country where a significant portion of the workforce is employed in agriculture, prompt identification and management of such diseases are essential. It used to take a lot of time and effort to diagnose diseases in apple plants via laboratory testing. Machine Learning (ML) methods offer a fast and accurate detection of diseased leaves in the apple orchard. This study aimed to develop a robust Convolutional Neural Network (CNN) model for identifying apple leaf diseases. A dataset comprising 1,532 images categorized into Healthy, Powdery mildew, and Rust classes was used. The CNN model consisted of six convolutional layers, six max-pooling layers, a flatten layer, and fully connected layers. Images were pre-processed (resized to 256x256 pixels, normalized, and augmented) to improve computational efficiency. The model was evaluated using metrics such as accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved a training accuracy of 98.02%, validation accuracy of 85.17%, and overall accuracy of 91.34%. Precision and recall for individual classes ranged from 86.05% to 96.55%. F1-scores showed balanced performance across categories, with a weighted average of 92.54%. These results demonstrate the model's effectiveness in classifying leaf conditions and its potential to enhance disease management in apple orchards and similar crops.

Method: This research aimed to create a model for identifying diseases in apple orchards. The model used damaged leaves exhibiting characteristics and symptoms of two groups of diseases: Powdery mildew and Rust. A new class, called the Healthy Leaf class, was included for comparison. This work constructed a CNN model that had 6 convolutional layers, 6 max-pooling layers, flatten and fully connected layers. The model's performance was evaluated using measures such as accuracy, precision, F1-score, and confusion matrix.

Results:After 25 epochs, the CNN model architecture shows 98.02% training and 85.17% validation accuracy for apple leaf disease identification. An overall accuracy of 91.34% demonstrates the effectiveness of the model. This work is useful for leaf disease management in apples or other trees.