Computer Vision Based Mango Fruit Grading System

(Mangifera Indica L.) fruit grading is proposed. In this system several features which are sensitive to the maturity level, size and surface defects were extracted. For maturity prediction Recursive Feature Elimination (RFE) technique with Support Vector Machine (SVM) based classifier has been employed. Size and surface defects are determined using several image processing method. Finally to solve the multi characteristics problem, Multi Attribute Decision Making (MADM) theory was adopted in this system. The results show that size detection error is nearly 3%, maturity prediction accuracy 96%, and surface defect accuracy 92%. The performance accuracy for grading by the proposed system is nearly 90% if expert grading is assumed to be 100% accurate. However, this variation is due to subjective judgment of expert-beings in perceiving the mango visually, which of course is obvious. Moreover, the repeatability of the proposed system is found to be 100%.

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