Vision Based Volume Estimation Method For Automatic Mango Grading System

In horticulture, measuring, sorting by shape, and determining the size and volume of fruits are all essential processes for meeting market quality standards and increasing market value. Fruit sorting and grading processes are very laborious and time-consuming task but machine vision-based fruit grading systems have the potential to replace human labour. However, a great challenge in vision-based fruit grading system is the recognition of different features such as shape, size, skin flaw and sometimes even three-dimensional (3D) shape. In this study, a simple and efficient image processing algorithm is proposed for estimating volume and 3D shape of mango fruit. The width and length of mango fruit are obtained from two-dimensional (2D) colour image. Then, fruit thickness is estimated based on light intensity distribution in 2D (top view) of mango fruit and maximum width-thickness correlation. The 3D shape of the mango fruit is then reconstructed. The accuracy of proposed method was compare with two existing volume prediction methods. Estimated volumes were compared with measured measurements using water-displacement method and the reconstructed 3D shapes were compared with measured structures of mango fruits. For a total of 150 mango samples, the results show that the proposed method gave an accuracy of 96.8% whilst the two other methods gave 91.7% and 91.5% respectively. The reconstructed mango shapes were therefore in close agreement with measured shapes. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.