基于机器视觉的香蕉果肉缺陷预测方法  

Prediction method of banana pulp defect by machine vision

作　　者：张铮[1] 熊盛辉 王孙强 胡凌辉 ZHANG Zheng;XIONG Sheng-hui;WANG Sun-qiang;HU Ling-hui(School of Mechanical Engineerin,Hubei University of Technology,Wuhan,Hubei 430068,China)

机构地区：[1]湖北工业大学机械工程学院,湖北武汉430068

出　　处：《食品与机械》2020年第7期150-154,共5页Food and Machinery

基　　金：国家自然科学基金项目(编号:61976083)。

摘　　要：针对香蕉内部果肉缺陷难以预测的问题,运用机器视觉技术对香蕉果皮与果肉进行图像识别,对识别参数进行数据拟合得到果肉缺陷的预测模型。将采集到的图像灰度化并进行滤波去噪,通过双阀值二值化和形态学分析对图像进行识别处理,提取香蕉果皮、香蕉果肉、香蕉果皮黑斑与香蕉果肉缺陷。计算提取区域的像素点总数,将其作为区域面积。分别用香蕉果皮总面积/香蕉果肉总面积与果皮黑斑面积/果肉缺陷面积之比来定义香蕉果皮黑斑度与果肉缺陷度。运用多项式拟合法,根据训练样本得出果肉缺陷预测函数,对预测函数进行残差分析。通过预测模型对香蕉划分等级,总准确率达到88.9%,与通过香蕉果皮进行等级划分其他方法相比,试验所得模型的预测准确率较高,表明通过香蕉果肉进行预测的方法具有一定的优越性。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.

关 键 词：香蕉 机器视觉 缺陷预测 多项式拟合 

分 类 号：S668.1[农业科学—果树学] TP391.41[农业科学—园艺学]