Abstract
In view of the problem that it is difficult to predict the inner pulp defect of banana, the machine vision technology is employed to recognize the image of banana peel and pulp, and then fits the recognition parameters to get the prediction model of pulp defect. The collected image, grayed and filtered, is recognized by double threshold and morphological analysis to extract banana peel, banana pulp, banana peel black spot and banana pulp defect. Thereafter, the total number of pixels in the extracted region is calculated, and the total number of pixels is taken as the area of the region. The ratio of the total area of banana peel / total area of banana pulp to the area of black spot of banana peel / defect area of banana pulp is used to define the degree of black spot of banana peel and defect of banana pulp. Using polynomial fitting method, the prediction function of pulp defect is obtained according to the training samples, and the residual analysis is carried out. The accuracy of banana grading was 88.9%. Compared with the other method to predict the defection of class by peel of banana, the accuracy of prediction mathematic is better, indicating the more practice value of predicting by flesh of banana in this study.
Publication Date
7-28-2020
First Page
150
Last Page
154
DOI
10.13652/j.issn.1003-5788.2020.07.031
Recommended Citation
Zheng, ZHANG; Sheng-hui, XIONG; Sun-qiang, WANG; and Ling-hui, HU
(2020)
"Prediction method of banana pulp defect by machine vision,"
Food and Machinery: Vol. 36:
Iss.
7, Article 31.
DOI: 10.13652/j.issn.1003-5788.2020.07.031
Available at:
https://www.ifoodmm.cn/journal/vol36/iss7/31
References
[1] 罗印斌,蔡艳丽,兰菡,等.农产品无损检测方法应用现状[J].食品工业科技,2018,39(15):340-344.
[2] 杨涛,张云伟,苟爽.基于机器视觉的草莓自动分级方法研究[J].食品与机械,2018,34(3):146-150.
[3] 项辉宇,薛真,冷崇杰,等.基于Halcon的苹果品质视觉检测试验研究[J].食品与机械,2016,32(10):123-126.
[4] 李国进,董第永,陈双.基于计算机视觉的芒果检测与分级研究[J].农机化研究,2015,37(10):13-18,23.
[5] 李江波.脐橙表面缺陷的快速检测方法研究[D].杭州:浙江大学,2012:16-21.
[6] 胡孟晗,董庆利,刘宝林,等.基于计算机视觉的香蕉贮藏过程中颜色和纹理监测[J].农业机械学报,2013,44(8):180-184.
[7] 赵文锋,朱菊霞,董杰.基于图像处理的香蕉成熟度检测系统[J].现代农业装备,2016(5):33-36.
[8] SHREYA P,ANIL K,VARUN B,et al.A context sensitive multilevel thresholding using swarm based algorithms[J].IEEE/CAA Journal of Automatica Sinica,2019,6(6):1 471-1 486.
[9] 熊炜,王鑫睿,王娟,等.融合背景估计与U-Net的文档图像二值化算法[J].计算机应用研究,2020,37(3):896-900.
[10] 王蔚,王晓凯,龚真,等.基于形态学的机器视觉玻璃切割边缘提取[J].测试技术学报,2020,34(1):22-27.
[11] 孙建桐,孙意凡,赵然,等.基于几何形态学与迭代随机圆的番茄识别方法[J].农业机械学报,2019,50(增刊1):22-26,61.
[12] 许金鑫,由强.任意阶次多项式最小二乘拟合不确定度计算方法与最佳拟合阶次分析[J].计量学报,2020,41(3):388-392.