Stem and Calyx Identification of 3D Apples Using Multi-Threshold Segmentation and 2D Convex Hull

Abstract

Traditional machine vision is widely used to identify apple quality, but this method finds it difficult to distinguish the apple stem and calyx from defects. To address this, we designed a new method to identify the stem and calyx of apples based on their concave shape. This method applies a fringe projection in a computer vision system of 3D reconstruction, followed by multi-threshold segmentation and a 2D convex hull technique to identify the stem and calyx. A camera and projector were used to reconstruct the 3D surface of the front half of an inspected apple. The height information for each pixel was reconstructed by a fringe projection and mathematical transformation. The 3D-reconstructed result was subjected to a multi-threshold segmentation technique and the segmentation results contained a concave feature in the curved line, representing the concave stem and calyx. The segmentation results were then subjected to a 2D convex hull technique, allowing for the identification of the stem and calyx. This method was evaluated using four groups of apples, and the proposed method is able to identify the stem and calyx with 98.93% accuracy.