草莓重量和形状图像特征提取与在线分级方法

doi:10.16182/j.issn1004731x.joss.17-0047

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (1): 7-9.doi: 10.16182/j.issn1004731x.joss.17-0047

草莓重量和形状图像特征提取与在线分级方法

张青¹, 邹湘军², 林桂潮¹, 孙艳辉¹

1. 滁州学院安徽省热敏性物料加工工程技术研究中心,安徽滁州 239000;
2. 华南农业大学南方农业机械与装备关键技术教育部重点实验室,广东广州 510642

收稿日期:2017-01-10 修回日期:2017-05-11 出版日期:2019-01-08 发布日期:2019-04-16
作者简介:张青(1990-),女,安徽安庆,硕士,助教,研究方向为智能装备设计与制造。
基金资助:
国家自然科学基金(31571568), 安徽省热敏性物料加工工程技术研究中心开放课题(2015RMZ03), 滁州学院校级规划(2016GH10, 2016GH11)

Image Feature Extraction and Online Grading Method for Weight and Shape of Strawberry

Zhang Qing¹, Zou Xiangjun², Lin Guichao¹, Sun Yanhui¹

1. Heat-sensitive Materials Processing Engineering Technology Research Center of Anhui, Chuzhou University, Chuzhou 239000, China;
2. Key Lab of Key Technology on South Agricultural Machine and Equipment Ministry of Education, South China Agricultural University, Guangzhou 510642, China

Received:2017-01-10 Revised:2017-05-11 Online:2019-01-08 Published:2019-04-16

摘要/Abstract

摘要： 针对草莓在采后分级生产中存在分级规格不一和效率低下等问题,提出一种基于机器视觉技术的草莓重量与形状分级方法。利用阈值分割法检测草莓果实,提取果实周长和面积参数,通过多元线性回归分析建立草莓重量分级模型;提取果实的低频椭圆傅里叶系数作为形状特征参数,并对支持向量机进行训练,建立草莓形状分级模型。选用200个草莓样本进行试验,结果表明：重量分级正确率为89.5%,形状分级正确率为96.7%,平均运算时间分别为64 ms和39 ms。试验验证了该方法的鲁棒性和实时性。

关键词: 机器视觉, 分级, 草莓, 凸包, 椭圆傅里叶描述子, 支持向量机

Abstract: To deal with the classification problems of strawberry in production, a machine vision based strawberry weight and shape grading method was proposed. The strawberry image was segmented by thresholding to extract the fruit. The area and perimeter parameters of the fruit were then calculated and used to build the strawberry weight grading model through regression analysis. Elliptic Fourier descriptor was used to extract the shape features of the fruit, and these shape features were applied to train a support vector machine (SVM) which represented the strawberry shape grading model. 200 samples of strawberries were selected to test both models, and the results showed that the weight grading accuracy was 89.5%, the shape grading accuracy was 96.7%, and the average calculation time were 64 ms and 39 ms, respectively. Therefore, the approaches for grading strawberries were robust and effective.

Key words: machine vision, strawberry, grading, convex hull, elliptic Fourier descriptor, support vector machine

中图分类号:

TP391.41

张青, 邹湘军, 林桂潮, 孙艳辉. 草莓重量和形状图像特征提取与在线分级方法[J]. 系统仿真学报, 2019, 31(1): 7-9.

Zhang Qing, Zou Xiangjun, Lin Guichao, Sun Yanhui. Image Feature Extraction and Online Grading Method for Weight and Shape of Strawberry[J]. Journal of System Simulation, 2019, 31(1): 7-9.

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草莓重量和形状图像特征提取与在线分级方法

Image Feature Extraction and Online Grading Method for Weight and Shape of Strawberry

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