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A method for classifying citrus surface defects based on machine vision

文献类型： 外文期刊

作者： Zhang, Wenzhuo 1 ; Tan, Aijiao 1 ; Zhou, Guoxiong 1 ; Chen, Aibin 1 ; Li, Mingxuan 1 ; Chen, Xiao 1 ; He, Mingfang 1 ; Hu 1 ;

作者机构： 1.Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha, Peoples R China

2.Cent South Univ Forestry & Technol, Hunan Prov Key Lab Urban Forest Ecol, Changsha, Peoples R China

3.Hunan Acad Agr Sci, Inst Plant Protect, Changsha, Hunan, Peoples R China

关键词： Citrus surface defects; Convolutional neural network; Machine vision; FCM algorithm; GWO algorithm; State Transition algorithm

期刊名称：JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION （ 影响因子：1.648； 五年影响因子：1.649 ）

ISSN： 2193-4126

年卷期： 2021 年 15 卷 3 期

页码：

收录情况： SCI

摘要： When detecting citrus surface defects, the performance of machine vision system is affected by different aspects such as the size, shape and environment. Therefore, a method for classifying citrus surface defects based on machine vision was proposed in this paper. First, the Fuzzy C-Means algorithm optimized by the Gray Wolf Optimizer algorithm was used to preprocess the citrus image. The citrus in the image was separated from the background; Then, the improved convolutional neural network combined with the State Transfer Algorithm (STA) was used to identify the citrus surface defects. We selected 2000 Tribute Citrus, 1000 ones with and without the defects separately, to carry on the experiment. The identification accuracy of the trained model on the dataset was 99.1%. In order to verify the effectiveness of the model in complex background, the convolutional neural network in combination with a STA was compared with SVM, AlexNet, VGG16 and other methods. The experimental results show that the citrus surface defect classification method based on machine vision is effective.

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