Comparative Analysis of Data Augmentation Strategies for Corn Leaf Disease Classification Using Transfer Learning

The choice of data augmentation can change how a transfer-learning model represents disease symptoms, particularly when classes differ through subtle lesion color and shape. This study evaluated five training conditions for MobileNetV2-based corn leaf disease classification: no augmentation, geometric augmentation, color augmentation, combined augmentation, and strong augmentation. Experiments used 4,188 PlantVillage corn leaf images from the Blight, Common Rust, Gray Leaf Spot, and Healthy classes. A fixed stratified 70:20:10 split and the same two-stage training protocol were applied in every condition, so the augmentation policy was the only experimental factor. Performance was assessed from test accuracy, precision, recall, F1-score, confusion matrices, and error counts. Color augmentation produced the strongest result, reaching 92.60% accuracy, a 91.42% macro F1-score, a 92.52% weighted F1-score, and 31 misclassified images. It also raised the Gray Leaf Spot F1-score from 82.35% without augmentation to 84.91%. Geometric augmentation offered no gain, whereas the combined and strong policies reduced overall performance. For this dataset, moderate brightness and contrast variation preserved disease structure more effectively than broader spatial or aggressive transformations.