Leaf Disease Detection in Blueberry Using Efficient Semi-supervised Learning Approach

Abstract

Blueberry leaf disease detection is really important to help farmers to early detect leaf disease and find a suitable method to cure the disease. Therefore, this research introduces an approach to detect and classify blueberry leaf disease by using an unsupervised method (auto-encoder), and a supervised method (support vector machine). The accuracy of our proposed method was evaluated by conducting the experiments on blueberry dataset captured at Can Tho City, Vietnam. The existing augmentation techniques was also applied to increase the data size of training and testing. For the first experiment on normal capturing conditions, the F1 scores of the proposed method and SVM are 89.28% and 81.48%, respectively. For the second experiment with noisy conditions, the F1 scores of the proposed method and SVM are 81.5% and 66.7%, respectively.