Apple fruit size estimation using a 3D machine vision system

Abstract

Estimation of fruit size in tree fruit crops is essential for selective robotic harvesting and crop-load estimation. Machine vision systems for fruit detection and localization have been studied widely for robotic harvesting and crop-load estimation. However, only a few studies have been carried out to estimate fruit size in orchards using machine vision systems. This study was carried out to develop a machine vision system consisting of a color CCD camera and a time-of-flight (TOF) light-based 3D camera for estimating apple size in tree canopies. As a measure of fruit size, the major axis (longest axis) was estimated based on (i) the 3D coordinates of pixels on corresponding apple surfaces, and (ii) the 2D size of individual pixels within apple surfaces. In the 3D coordinates-based method, the distance between pairs of pixels within apple regions were calculated using 3D coordinates, and the maximum distance between all pixel pairs within an apple region was estimated to be the major axis. The accuracy of estimating the major axis using 3D coordinates was 69.1%. In the pixel-size-based method, the physical sizes of pixels were estimated using a calibration model developed based on pixel coordinates and the distance to pixels from the camera. The major axis length was then estimated by summing the size of individual pixels along the major axis of the fruit. The accuracy of size estimation increased to 84.8% when the pixel size-based method was used. The results showed the potential for estimating fruit size in outdoor environments using a 3D machine vision system.