Hierarchical approach for ripeness grading of mangoes

作     者：Anitha Raghavendra D.S.Guru Mahesh K.Rao R.Sumithra 

作者机构：Maharaja Institute of Technology MysoreBelawadiS.R.Patna TalukMandya 571477India Department of Studies in Computer ScienceManasagangothriUniversity of MysoreMysore 570006India 

出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)

年 卷 期：2020年第4卷第1期

页      面：243-252页

核心收录：

学科分类：08[工学] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We thank all the reviewers for their critical cum encouraging comments which helped us a lot in significant improvement on quality and readability of the content of the manuscript 

主　　题：Alphonso mango L*a*b color space Threshold based classifier Support vector machine 

摘      要：Grading of fruits based on their ripeness has been a topic of research for the last two *** the ripened mangoes has become more of an art than science and is a challenging *** study aims at introducing a system to grademangoes with four classes based on their *** study was demonstrated through an extensive experimentation on a newly created dataset consisting of 981 images of Alphonsomango variety belonging to four classes viz.,under-ripen,perfectly ripen,over-ripen with internal defects and over-ripen without internal *** this study,a hierarchical approach was adopted to classify the mangoes into the four *** each stage of classification,L*a*b color space features were *** the purpose of classification at each stage,a number of classifiers and their possible combinationswere tried *** study revealed that,the Support VectorMachine(SVM)classifier works better for classifyingmangoes into under-ripen,perfectly ripen and overripen while the thresholding classifier has a superior classification performance on over-ripen with internal defects and over-ripen without internal ***,to bring out the superiority of the hierarchical approach,a conventional single shot multi-class classification approach with SVMwas also *** results of the experimentation demonstrated that the hierarchical method with an accuracy of 88%outperforms the counterpart conventional single shot multi-class classification approach in addition to several existing contemporary models.