Ensemble transfer learning for plant leave disease identification

Plant diseases result in significant economic losses each year. The common plant diseases include early and late blight. As an example, early blight is caused by fungus while light blight is caused by a specific microorganism. If the plant diseases are detected in early stages with appropriate treatment, such economic loss could be prevented. Therefore, in this research, we propose an ensemble model combining three transfer learning networks, i.e. Resnet50, VGG-16, and MobileNetv2, for plant leaf disease identification. Evaluated using the Plant Village dataset, the proposed ensemble transfer learning model achieves impressive performance for the detection of healthy and unhealthy plant leaves with improved accuracy rates.