Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model

Intelligent detection is a key technology in precision agriculture. As items of different color cluster in different non-overlapping elliptical regions, this study proposed a method for constructing a multi-elliptical boundary model in Cr-Cb co-ordinates to detect citrus fruit and tree trunks in natural light environments. Here, the detected citrus variety was spring sweet tangerine, and the parameters of the elliptical boundary models for detecting these fruit and tree trunks solved by color-space transformation and ellipse fitting. A series of image detection experiments were performed to evaluate the method's performance. The experimental results showed that the correct and false positive percentages in fruit identification from images were 90.8 and 11.2%, respectively. The number of correctly detected images in distinguishing tree trunks from background was 44 of 50 images.