Hierarchical approach for ripeness grading of mangoes

• Main Authors: Anitha Raghavendra, D.S. Guru, Mahesh K. Rao, R. Sumithra
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2020-01-01
• Series: Artificial Intelligence in Agriculture
• Subjects: Alphonso mango; L*a*b color space; Threshold based classifier; Support vector machine
• Online Access: http://www.sciencedirect.com/science/article/pii/S2589721720300301

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 grade mangoes 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 Alphonso mango 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 combinations were tried out. The study revealed that, the Support Vector Machine (SVM) classifier works better for classifying mangoes into under-ripen, perfectly ripen and over-ripen 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 SVM was 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.