A method for classifying citrus surface defects based on machine vision

被引：0
作者：
Wenzhuo Zhang
Aijiao Tan
Guoxiong Zhou
Aibin Chen
Mingxuan Li
Xiao Chen
Mingfang He
Yahui Hu
机构：
[1] Central South University of Forestry and Technology,College of Computer and Information Engineering
[2] Central South University of Forestry and Technology,Hunan Provincial Key Laboratory of Urban Forest Ecology
[3] Hunan Academy of Agricultural Sciences,Plant Protection Institute
来源：
Journal of Food Measurement and Characterization | 2021年 / 15卷
关键词：
Citrus surface defects; Convolutional neural network; Machine vision; FCM algorithm; GWO algorithm; State Transition algorithm;
D O I：
暂无
中图分类号：
学科分类号：
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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页码：2877 / 2888
页数：11
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