CNN-BASED DETECTION OF POWDERY MILDEW AND RUST IN APPLE ORCHARDS FOR OPTIMIZING CROP MANAGEMENT

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. Keywords: Machine Learning, Apple Orchard, Crop Management, Convolutional Neural Network, Evaluation Metrics