Ginger Leaf Diseases Detection Using Deep Learning: A Comparative Study of Pre-Trained Models

Ginger (Zingiber officinale) is an essential crop that is widely cultivated for its medical and culinary value. In 2023, ginger was considered one of the highest value herbs, with approximately 9089.85 tons produced in Malaysia. However, the ginger cultivation suffers from plant diseases, which lead to plant death and eventually cause crop losses. Furthermore, the lack of studies in ginger leaf disease detection using deep learning techniques is a limitation that hinders the early diagnosis and management of ginger diseases. To address this limitation, we collected 968 ginger plant images cropped into single leaf images and labelled into 4 classes: leaf blight, dehydrated, damaged pest, and healthy, using the Encordplatform. The generated dataset consisted of 4033 leaf images. Through data augmentation, the dataset was expanded into 10,910 leaf images to improve the model’s generalization. As deep learning techniques are popular in plant disease detection, we evaluated several popular pre-trained models using TensorFlow and PyTorch libraries and compared the performance with that of other models. For all of these models, the same settings were applied with minimal modification to the model’s layers. Among the compared models, EfficientNetB3 achieved the highest accuracy of 94.3% in detecting ginger leaf diseases. It surpassed other models and exceeded the next-best model in this experiment, MobileNetV2, which achieved 89.66% accuracy, by 4.64%.