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.
K1 Machine Learning, Apple Orchard, Crop Management, Convolutional Neural Network, Evaluation Metrics PB Pakistan Agricultural Scientists Forum LK https://thejaps.org.pk/AbstractView.aspx?mid=2024-JAPS-2099