Application of Transfer Learning Using Inception-Resnet-V2 for Image-Based Classification of Apple Leaf Diseases

Abstrak

Apple leaf diseases posed a major challenge to agricultural productivity due to their similar visual appearance and the limitations of manual classification methods. This study aimed to develop an accurate and efficient image-based classification system for apple leaf diseases using the Inception-ResNet-V2 architecture and a transfer learning approach. The dataset consisted of 3,171 images from the PlantVillage dataset, categorized into four classes: Apple Scab, Cedar Apple Rust, Black Rot, and Healthy. The data were divided into training, validation, and test sets in a 70:15:15 ratio using stratified sampling. Image preprocessing included resizing, normalization, and data augmentation, while class balancing was applied to address class imbalance. The model was trained using the Adam optimizer through a two-stage process consisting of feature extraction and refinement. Experimental results showed that the proposed model achieved a test accuracy of 98.74%, with high precision, recall, and F1-scores across all classes, demonstrating strong classification performance and generalization ability. This study demonstrated that Inception-ResNet-V2 was effective in capturing complex visual features of apple leaf diseases. In conclusion, the proposed approach offered an effective solution for classifying apple leaf diseases and had the potential to support more efficient and accurate agricultural decision-making.