Grading of fruits based on their ripeness has been a topic of research for the last two decades.Identifying the ripened mangoes has become more of an art than science and is a challenging task.This study aims at intro...Grading of fruits based on their ripeness has been a topic of research for the last two decades.Identifying the ripened mangoes has become more of an art than science and is a challenging task.This study aims at introducing a system to grademangoes with four classes based on their ripeness.The 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 defects.In this study,a hierarchical approach was adopted to classify the mangoes into the four classes.At each stage of classification,L*a*b color space features were extracted.For the purpose of classification at each stage,a number of classifiers and their possible combinationswere tried out.The 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 defects.Further,to bring out the superiority of the hierarchical approach,a conventional single shot multi-class classification approach with SVMwas also studied.The 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.展开更多
文摘Grading of fruits based on their ripeness has been a topic of research for the last two decades.Identifying the ripened mangoes has become more of an art than science and is a challenging task.This study aims at introducing a system to grademangoes with four classes based on their ripeness.The 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 defects.In this study,a hierarchical approach was adopted to classify the mangoes into the four classes.At each stage of classification,L*a*b color space features were extracted.For the purpose of classification at each stage,a number of classifiers and their possible combinationswere tried out.The 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 defects.Further,to bring out the superiority of the hierarchical approach,a conventional single shot multi-class classification approach with SVMwas also studied.The 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.
