Automated Asian Fruit Grading System Using Stereo Vision Technique

• Main Authors: Abd Rashid, A.N (Author), Abdullah M. (Author), Amir, F. (Author), Harron, N.A (Author), Mohd Saod, A.H (Author), Ramlan, S.A (Author)
• Format: Article
• Language: English
• Published: Institute of Physics Publishing, 2020
• Subjects: Cameras; Conventional methods; Fruits; Grading; Image segmentation; Information management; Mathematical morphology; Mean absolute percentage error; Mean square error; Morphological operations; Regression analysis; Regression method; Root mean squared errors; Semiconductor materials; Stereo image processing; Stereo vision; Stereo vision system; Triangulation method; Weight estimation
• Online Access: View Fulltext in Publisher

 In Malaysia, most of fruit planters are still using the conventional methods of inspection and grading their products by manual weightage measurement and visual inspection. Conventional methods rely on human eyes to perform the grading which leads to unproductive system and lack of data management. Therefore, in this study, an automated weight grading system is proposed to perform the grading system via size measurement using stereo vision system and image processing. The fruit selected to verify the system proposed performance were fruit samples provided by MARDI Bukit Tangga located in Kedah, Malaysia. In the vision system, triangulation method is used where the samples were graded more accurately using two cameras to form a stereo vision system. The image capture through the vision system processed using multiple image processing steps such as image segmentation, sobel edge sensor and multiple morphological operation. The relationship between weight and area of fruits done via regression methods with adjusted R-square of 0.9823. Using this goodness of fit, weight estimation can be executed with Root Mean Squared Error (RMSE) of 6.811. Overall, fruit grading system with the stereo vision of a low-cost web camera implementation has successfully performed with the accuracy of 85.65% and a Mean Absolute Percentage Error (MAPE) of 6.551%. © 2020 IOP Publishing Ltd. All rights reserved.