Rapid and accurate fruit volume estimation using a top-mounted mirror-based imaging system

Abstract Size is one of the important external quality criteria of fruit and is often used in grading and sorting processes. Computer vision offers an effective solution for quickly and non-destructively estimating fruit volume. Several approaches involving three-dimensional (3D) reconstruction models of irregularly shaped fruit from multiple side-view images have been employed to enhance estimation accuracy. However, these approaches tend to be expensive and computationally complex. This study proposes a low-cost multi-view system for the rapid and accurate estimation of fruit volume. Using a top-mounted mirror-based setup, the system captures multiple fruit surfaces with a single camera, eliminating the need for rotation or multiple cameras. The captured multi-view image is analyzed using a global thresholding technique to calculate the multi-view area. A simple linear regression model is then built to estimate the fruit’s volume based on multi-view area, without requiring 3D model reconstruction. The proposed system was successfully tested for estimating the volume of two irregularly shaped fruits (pomelo and mango), achieving high coefficients of determination (0.986 and 0.988, respectively). Additionally, the system was tested with different fruit orientations, and the results showed that orientation did not affect volume estimation. These results demonstrate that this approach has strong potential not only for fruit quality assessment but also for other irregularly shaped solid objects where rapid and accurate volume estimation is needed.