Automatic grading of apples based on multi-features and weighted K-means clustering algorithm

作     者：Yang Yu Sergio A.Velastin Fei Yin 

作者机构：College of Information and Management ScienceHenan Agricultural UniversityZhengzhou 450046PR China Collaborative Innovation Center of Henan Grain CropsZhengzhouHenan 450002PR China Queen Mary University of LondonMile EndLondon E14NSUK 

出 版 物：《Information Processing in Agriculture》 (农业信息处理（英文）)

年 卷 期：2020年第7卷第4期

页      面：556-565页

核心收录：

学科分类：0907[农学-林学] 0908[农学-水产] 09[农学] 090201[农学-果树学] 0710[理学-生物学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0905[农学-畜牧学] 0707[理学-海洋科学] 0906[农学-兽医学] 0829[工学-林业工程] 0901[农学-作物学] 0902[农学-园艺学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was mainly supported by the Collaborative Innovation Center of Henan Grain Crops,Zhengzhou and by the National Key research and development programof China(No.2017YFD0301105) Key science and Technology Program of Henan Province(No.192102110196) 

主　　题：Apple grading Multi-features Threshold segmentation K-means 

摘      要：In this paper,a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of *** method provides a novel way of using four images(top,bottom and two sides)and average gray values for each apple to distinguish between the apple defects,stem and ***,weighted features(MCSAD(maximum cross-sectional average diameter),circularity,PRA(proportion of red area)and defect regions)were carefully selected according to the requirements of the national apple grading standard,which improves the practicality of the proposed ***,qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%,which provides encouragement for the additional research and implementation of multifeature automatic grading for the fruit industry.