Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning

Abstract

Rapid and accurate identification of wheat leaf diseases and their severity is benefit for the precise prevention and control of wheat leaf diseases. Taking powdery mildew and stripe rust as research objects, this study proposes an algorithm for identification of wheat leaf diseases and their severity based on Elliptical-Maximum Margin Criterion (E-MMC) metric learning. Compared with other metrics, elliptic metric combined with MMC can find the non-linear transformation that reflects the spatial structure or semantic information of the wheat leaf disease image, which can enlarge the distance between different classes and better complete the identification task. In the proposed algorithm, Otsu method is used to segment the disease spots according to the characteristics of disease distribution in wheat leaf images. Moreover, the best combination of color and texture features in the wheat disease spot image is determined to construct training set. By using the maximum margin criterion and gradient rise method, the optimal elliptic metric matrix is obtained, thereby transforming the sample feature space and reducing the correlation between features. Then, the wheat powdery mildew, stripe rust, and their severity are identified. The experimental results show that the proposed algorithm is superior to the traditional support vector machines and other algorithms. The highest identification accuracy obtained by the proposed algorithm is 94.16 %. These findings can provide valuable help for the intelligent identification and classification of wheat leaf diseases.