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Sensors 2016, 16(11), 1753; doi:10.3390/s16111753

In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging

1
Center of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Taman Muhibbah, Jejawi, Arau, Perlis 02600, Malaysia
2
School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, Perlis 02600, Malaysia
3
School of Bioprocess Engineering, Universiti Malaysia Perlis (UniMAP), Kompleks Pusat Pengajian Jejawi 3, Kawasan Perindustrian Jejawi, Arau, Perlis 02600, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 2 June 2016 / Revised: 9 July 2016 / Accepted: 18 July 2016 / Published: 27 October 2016
(This article belongs to the Special Issue Sensors for Agriculture)
View Full-Text   |   Download PDF [3502 KB, uploaded 27 October 2016]   |  

Abstract

The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. View Full-Text
Keywords: machine vision; mango; Fourier descriptor; disk method; shape; volume estimation; image processing; mass estimation machine vision; mango; Fourier descriptor; disk method; shape; volume estimation; image processing; mass estimation
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MDPI and ACS Style

Ibrahim, M.F.; Ahmad Sa’ad, F.S.; Zakaria, A.; Md Shakaff, A.Y. In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging. Sensors 2016, 16, 1753.

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