Open AccessRapid Recognition of Tomato’s Disease Stages Based on the Kernel Mutual Subspace Method
Author(s) -
Yan Zhang,
Haiyuan Wu,
Xiaomin Wang
Publication year - 2021
Publication title -
applied engineering in agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.276
H-Index - 54
eISSN - 1943-7838
pISSN - 0883-8542
DOI - 10.13031/aea.14507
Subject(s) - pattern recognition (psychology) , principal component analysis , subspace topology , artificial intelligence , support vector machine , kernel (algebra) , computer science , feature vector , kernel principal component analysis , gaussian function , random subspace method , canonical correlation , dimensionality reduction , kernel method , mathematics , gaussian , physics , combinatorics , quantum mechanics
HighlightsA low time complexity and high accuracy recognition method of tomato’s disease stages was proposed. Small number of disease image samples can support a stable and high accuracy method with less computation time and less memory cost. Provided a solution to establish a real-time and low energy consumption disease recognize system.Abstract. 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 extracted, and the kernel mutual subspace method (KMSM) was introduced to establish a rapid Tomato’s disease stage recognition method. Firstly, the color and textural features were extracted 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. Keywords: Color and textural feature, High dimensional feature space, Kernel mutual subspace method, Tomato’s disease stages recognition,.