In this study, deep learning models are trained for apple leaf disease classification with challenges like light variation, shadows, similar disease symptoms and intraclass variability. Conventional methods are time-consuming and require experts with technical knowledge. The use of transfer learning is explored to improve model accuracy across different datasets with different characteristics, such as class imbalance, image quality, and augmentation. For this purpose, five pre-trained CNN architectures, InceptionV3, ResNet50V2, Xception, MobileNetV2, and InceptionResNetV2, were evaluated. Models’ performance was measured using accuracy, precision, recall, and F1-score, analyzing how augmentation, environmental noise, and class imbalances affect classification. Some models effectively distinguished diseases, while others have demonstrated stability against augmentation distortions. A practical apple leaf disease detection platform was developed, allowing users to upload images for automated classification. It incorporates the use of multiple models in a composite scoring system to improve the reliability of the decision-making process. This research compares the strengths and weaknesses of various CNN architectures and shows how deep learning can be useful in precision agriculture, plant disease control, and scalable disease management.