Identification of Stripe Rust and Leaf Rust on Different Wheat Varieties Based on Image Processing Technology

Abstract

The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases.

Epidemiology and Control of Fungal Diseases in Crop Plants

“…However, plant disease image recognition is affected by many factors. Wang et al [6] explored the effects of wheat varieties on image-based identification of two important wheat diseases including stripe rust and leaf rust caused by Puccinia triticina (Pt) and investigated an image-based method for identifying the two diseases on different wheat varieties. A total of 9087 single-leaf disease images were acquired, including the images of stripe rust and leaf rust from wheat varieties Beijing 0045, Mingxian 169, and Nongda 211 in the field in Shangzhuang Experimental Station of China Agricultural University, Beijing, China, and the controlled-climate chamber in the Laboratory of Macro-Phytopathology, China Agricultural University, Beijing, China, and the images of stripe rust from wheat varieties Longjian 9822, Longjian 9825, and Tianxuan 66 in the field in Gangu Testing Station of the Institute of Plant Protection, Gansu Academy of Agricultural Sciences, Gangu, Gansu, China.…”