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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 86-97.doi: 10.12133/j.smartag.2020.2.3.202009-SA001

• Topic--Agricultural Artificial Intelligence and Big Data • Previous Articles     Next Articles

Rapid Recognition Model of Tomato Leaf Diseases based on Kernel Mutual Subspace Method

ZHANG Yan1,2,3(), LI Qingxue1,2,3(), WU Huarui1,2,3   

  1. 1.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    2.Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    3.Key Laboratory of Agri-informatics, Ministry of Agriculture and Rural Affairs, Beijing 100097, China
  • Received:2020-09-01 Revised:2020-09-28 Online:2020-09-30


Research on tomato disease recognition based on leaf images has been widely concerned in recent years, and with the development of machine learning and deep learning, researchers from various countries have proposed a variety of methods and models to solve this problem. In this research, a new approach by fusion color and texture features, and kernal mutual subspace method (KMSM) were introduced and a rapid recognition model of tomato leaf disease was established. The color and texture features introduced in this research including color moment (CM), color coherence vector (CCV) and histogram of oriented gradient (HOG) features. The CCHKMSM (CM+CCV+HOG+KMSM) model firstly mapped the extracted color and texture features from different classes of leaf disease data sets to high-dimensional space using gauss kernal function. Then the principal component of the mapped high-dimensional space was analysed, and the nonlinear disease characteristic space was generated. Finally, the diseases based on the minimum cosine angle of nonlinear feature space were identified. Validation experiment was conducted based on public agricultural disease data sets of PlantVillage, which providing 9 kinds of most commonly tomato leaf disease and 1 kind of healthy leaf image, and the filed took image include 3 kinds of tomato leaf diseases images. For experiment based on PlantVillage data set, the results showed that the CCHKMSM realized the most high recognize accuracy rate of 100% when the number of each class was 350. The training time cost and recognition time cost was 0.1540 s and 0.013 s, respectively. Meanwhile, experiments were conducted in the range of sample image numbers from 150 to 1000 images for each class, with step length of 50, and the obtained results showed that the average recognition rate was 99.14%. For experiment based on field took tomato diseases data sets, after segment original image into sub-size image, average recognize accuracy for the kind of diseases arrived at 96.21%, which was higher than other typical machine learning models such as SVM and KMSM, and at the same level by comparing with deep learning-based recognition methods. On the other hand, as an significant adventure of the proposed CCHKMSM model, the computing cost was low, both the training time and testing time were much lower than deep learning methods, and requirement is loss the system to run. As a conclusion, the proposed CCHKMSM model, has high potential to be applied in low-configuration equipment such as hand-held devices and edge computing terminals.

Key words: tomato leaf image, rapid diseases recognition, color and texture feature, kernel mutual subspace method

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