<![CDATA[Bean leaf disease classification remains challenging due to limited annotated datasets and high visual variability in lesion size, color intensity, illumination, leaf orientation, and background conditions. Although convolutional neural networks and transfer learning have shown promising results, many existing bean leaf disease classification methods still rely on single-stage fine-tuning and end-to-end softmax classification, which may reduce generalization on small agricultural datasets. Furthermore, the combined use of staged transfer learning, MixUp regularization, multi-level deep feature extraction, and classical machine learning classifiers remains insufficiently explored. This gap highlights the need for a more robust and validation-controlled framework to improve classification performance under limited-data conditions. Therefore, this study proposes a hybrid classification framework that integrates two-phase VGG19 transfer learning, validation-selected MixUp regularization, multi-level deep feature extraction, and classical machine learning classifiers. In the first phase, the pretrained VGG19 backbone is frozen to train a tasks-pecific classification head, while in the second phase, selected deeper layers are fine-tuned to adapt high-level representations to bean leaf disease patterns. Deep features extracted from multiple representation levels are classified using SVM, KNN, and LightGBM under a strict validation-based model selection protocol. Experiments on the public IBean dataset show that the proposed hybrid approach outperforms the end-to-end CNN baseline. The best configuration, combining two-phase transfer learning, MixUp regularization, and LightGBM, achieves 98.44% test accuracy on the IBean dataset. Additional validation on an independent Bean Leaf Disease Dataset achieves 98.33% test accuracy. These findings indicate that the proposed framework is effective for improving bean leaf disease classification performance.]]>
