An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification
This work addresses a domain-specific problem for stakeholders in agriculture by providing an incremental improvement in disease classification for apple crops.
The study tackled the problem of automatically classifying apple leaf diseases by proposing a transfer learning-based approach using EfficientNetV2S and data augmentation, achieving an accuracy of 99.21% on the PlantVillage dataset.
Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.