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

文献类型： 外文期刊

作者： Wang, Hongli 1 ; Jiang, Qian 1 ; Sun, Zhenyu 2 ; Cao, Shiqin 3 ; Wang, Haiguang 1 ;

作者机构： 1.China Agr Univ, Coll Plant Protect, Beijing 100193, Peoples R China

2.Gansu Acad Agr Sci, Inst Plant Protect, Lanzhou 730070, Peoples R China

3.Gansu Acad Agr Sci, Wheat Res Inst, Lanzhou 730070, Peoples R China

关键词： wheat stripe rust; wheat leaf rust; image processing; disease identification; machine learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 1 期

页码：

收录情况： SCI

摘要： 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.

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