Comparative Analysis of Pre-Trained CNN Architectures for Apple Foliar Disease Classification

Abstract

Apple foliar diseases can significantly impact crop yield and quality. Early and accurate disease detection allows for timely disease management, reducing losses. This paper presents a comparative analysis of pretrained Convolutional neural network (CNN) architectures for automated apple foliar disease classification from leaf images. Eight CNN models, including DenseNet121, DenseNet201, ResNet50V2, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2 and Xception, were evaluated using an apple leaf dataset with five disease classes - scab, rust, black rot, multiple diseases, and healthy. Models were trained using transfer learning by fine-tuning on the dataset with different hyperparameters. Comparative performance analysis across five cases with varying batch size, optimizer, learning rate and epochs was conducted. Results indicate MobileNet architecture achieved best accuracy of 97%, outperforming other models. This demonstrates the potential of using pretrained CNNs for robust apple disease classification to enable precision agriculture and improved crop health monitoring. Key Words: apple diseases, convolutional neural networks, transfer learning, image classification