Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion

Autor:	Huan Tao, Lei Shi, Zhongxiong Zhang, Fujie Zhang, Tao Qin, Li Lixia, Yinlong Zhu, Yuhao Lin
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Computer science Stability (learning theory) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Feature selection TP1-1185 Panax notoginseng taproot Biochemistry machine vision machine learning feature fusion image processing hierarchical model Analytical Chemistry Panax notoginseng Electrical and Electronic Engineering Instrumentation Extreme learning machine biology Artificial neural network business.industry Deep learning Chemical technology Pattern recognition biology.organism_classification Atomic and Molecular Physics and Optics Support vector machine ComputingMethodologies_PATTERNRECOGNITION Test set Artificial intelligence business
Zdroj:	Sensors; Volume 21; Issue 23; Pages: 7945 Sensors, Vol 21, Iss 7945, p 7945 (2021)
ISSN:	1424-8220
DOI:	10.3390/s21237945
Popis:	The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.
Databáze:	OpenAIRE
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