Design and test of the grading system for kernel-free white grapes with support vector machine

Authors

LI Ze-ping, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
GUO Jun-xian, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
GUO Yang, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
LI Xue-lian, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
ZHANG Liang-liang, College of Mechanical and Electronic Engineering, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China
HUANG Hua, College of Mathematic and Physics, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China

Abstract

Objective: A support vector machine （SVM）-based grading system for kernel-free white grape bunches was designed to counter the problems of low accuracy and efficiency of manual grading of kernel-free white grape bunches. Methods: The images were preprocessed using Gaussian filtering, edge detection, contour detection and other pre-processing methods, and the SVM model was used to extract the contour, color and other feature parameters of the kernel-free white grape bunches and compared the recognition effects under different parameters based on the SVM model. Results: It was shown that the best parameters of the model were Best c=2.00, Best g=0.24, coef （）=0 and d=3. The grading accuracy of the kernelless white grape bunches reached 96%. Conclusion: Compared with the traditional manual grading method, the reliability and stability of the proposed grading system had obvious advantages and could meet the grading requirements of kernel-free white grape bunches in practical production.

Publication Date

10-28-2021

First Page

106

Last Page

111,246

DOI

10.13652/j.issn.1003-5788.2021.10.019

Recommended Citation

Ze-ping, LI; Jun-xian, GUO; Yang, GUO; Xue-lian, LI; Liang-liang, ZHANG; and Hua, HUANG (2021) "Design and test of the grading system for kernel-free white grapes with support vector machine," Food and Machinery: Vol. 37: Iss. 10, Article 19.
DOI: 10.13652/j.issn.1003-5788.2021.10.019
Available at: https://www.ifoodmm.cn/journal/vol37/iss10/19

References

[1] 张琼琼, 魏佳, 张健, 等. 硫化氢熏蒸对无核白葡萄采后细胞壁及病害的影响[J]. 食品工业科技, 2020, 41（13）: 277-283.
[2] 李俊伟. 基于机器视觉技术的新疆鲜葡萄及葡萄干品质分析研究[D]. 乌鲁木齐: 新疆农业大学, 2014: 4-5.
[3] 李少昆, 王崇桃. 图象及机器视觉技术在作物科学中的应用进展[J]. 石河子大学学报（自然科学版）, 2002（1）: 79-86.
[4] 徐莹莹. 基于计算机视觉的甜瓜外观品质检测研究[D]. 兰州: 甘肃农业大学, 2011: 40-45.
[5] PENG Hong-xing, SHAO Yuan-yuan, CHEN Ke-ying, et al. Research on multi-class fruits recognition based on machine vision and SVM[J]. IFAC-Papers on Line, 2018, 51（17）: 817-821.
[6] 汪威, 刘亚川, 吕斌, 等. 一种去柄鲜香菇视觉分级系统设计[J]. 食品与机械, 2021, 37（3）: 105-111.
[7] 罗陆锋, 邹湘军, 王成琳, 等. 基于轮廓分析的双串叠贴葡萄目标识别方法[J]. 农业机械学报, 2017, 48（6）: 15-22.
[8] 李俊伟, 郭俊先, 胡光辉, 等. 基于机器视觉技术的单粒葡萄质量与果径预测分级研究[J]. 新疆农业科学, 2014, 51（10）: 1 862-1 868.
[9] 肖壮, 王巧华, 王彬, 等. 基于数字图像及随机最小二乘的红提串果粒尺寸检测分级方法[J]. 食品科学, 2018, 39（15）: 60-66.
[10] LI Min, WANG Qiao-hua, ZHU Jiang. Automatic recognition of grapes' size level based on machine vision[J]. Journal of Food Agriculture and Environment, 2012, 10（3）: 78-80.
[11] 陈英, 廖涛, 林初靠, 等. 基于计算机视觉的葡萄检测分级系统[J]. 农业机械学报, 2010, 41（3）: 169-172.
[12] 许国威, 马胜前, 危淑平. 基于LabVIEW与MATLAB的自适应滤波器设计研究[J]. 计算机技术与发展, 2011, 21（1）: 226-229, 233.
[13] 颜兵, 王金鹤, 赵静. 基于均值滤波和小波变换的图像去噪技术研究[J]. 计算机技术与发展, 2011, 21（2）: 51-53, 57.
[14] 吴雷磊. 基于数学形态学的细胞图像分割算法研究[D]. 成都: 电子科技大学, 2007: 10-12.
[15] 孙增光, 王士军, 周永鑫, 等. 基于图像处理的焊缝实时跟踪技术研究[J]. 制造技术与机床, 2019（5）: 144-148.
[16] 董延华, 刘靓葳, 宋和烨, 等. 基于正则筛选的OCR手机号码自动识别的研究[J]. 吉林师范大学学报（自然科学版）, 2019, 40（1）: 115-120.
[17] 童源. 稻瘟病孢子图像识别算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2015: 26-28.
[18] 张万红. 基于图像法的离体小麦叶片几何参数计算[J]. 浙江大学学报（农业与生命科学版）, 2018, 44（6）: 748-754.
[19] 孙刚. 基于支持向量机的多分类方法研究[D]. 大连: 大连海事大学, 2008: 12-15.
[20] 张帅. 基于SVM的网络舆情文本分类研究[D]. 曲阜: 曲阜师范大学, 2015: 30-35.