Vision-based Size Estimation and Centroid Positioning of Partially Occluded Fruits

Abstract

The objective of this study was to propose a simple and efficient image processing algorithm for estimating the size and centroid of partially occluded round fruits. In the proposed method, the size and centroid of partially occluded fruit were estimated based on the mathematical properties of the arc-radius. The experimental tests were conducted in a laboratory with orange, Sunkist, apple, and tomato fruits by setting different occlusion conditions. The occlusion percentage was varied between 0% and 90%. The accuracy and processing time of the proposed method were compared with that of the widely-used conic-section circle fitting method. The results showed that the proposed method resulted in an overall accuracy of 95.1% and processing time of 0.66 s, as opposed to 60.2% and 0.68 s, respectively, using the conic-section equation. Compared with the conic-section equation, the proposed method was able to give a more robust prediction, even for low resolution images.

Keywords

• automatic harvesting system
• conic-section curve-fitting
• fruit-picking robot
• fruit size estimation
• vision system