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RAPID RECOGNITION OF TOMATO'S DISEASE STAGES BASED ON THE KERNEL MUTUAL SUBSPACE METHOD

文献类型： 外文期刊

作者： Zhang, Yan 1 ; Wu, Huarui 1 ; Chen, Cheng 1 ; Wang, Xiaomin 1 ;

作者机构： 1.Minist Agr & Rural Affairs, Beijing Res Ctr Informat Technol Agr, Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agriinformat, Beijing, Peoples R China

关键词： Color and textural feature; High dimensional feature space; Kernel mutual subspace method; Tomato's disease stages recognition

期刊名称：APPLIED ENGINEERING IN AGRICULTURE （ 影响因子：0.985； 五年影响因子：1.02 ）

ISSN： 0883-8542

年卷期： 2021 年 37 卷 5 期

页码：

收录情况： SCI

摘要： Disease stages recognition of tomato is important for the timely diagnosis and prevention of tomato diseases. In this article, the color and texture features were exfracted, and the kernel mutual subspace method (KA/ISM) was introduced to establish a rapid Tomato 's disease stage recognition method. Firstly, the color and textural features were exfracted from tomato leaf and mapped to a high-dimensional space using a Gaussian kernel function. Secondly, a nonlinear disease feature subspace was established by applying principal component analysis (PCA) based on the mapped high-dimensional space. Finally, disease stages are recognized by calculating the canonical angles between the testing subspace and reference sub-spaces. To validate recognition rate of the proposed method, we conducted 10-fold cross-validation for experiments using the tomato disease sets contained in PlantVillage and artificial intelligence (AI) Challenger 2018 datasets. Also compared with the support vector machine (SVM) and VGG 16 methods, the results show that, accuracy of those three evaluated methods on the PlantVillage datasets are 99.34%, 89.2%, and 97%, respectively; and accuracy on the AI Challenger 2018 datasets are 98.66%, 71.14%, and 73.93%, respectively. Moreover, average training and recognition time of the proposed method is 0.1496 and 0.008 s, respectively, which is faster than SVM and VGG16 methods. In conclusion, the proposed method can be carried out in real-time recognition intelligent equipment which requires less memory space, less computation time, and with higher recognition rate, and low energy consumption.

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