Detecting ripe fruits under natural occlusion and illumination conditions.

• An algorithm for detecting ripe fruit under natural illumination and occlusion conditions is proposed. • A graying factor K is proposed to remove the complex background of colorful ripe fruits. • Improving the Hough Transform by selecting equidistant points and increasing judgement threshold. • A real contour Hough Transform was proposed to detect ripe fruits or occluded circles. For an accurate detection of ripe fruits under uneven illumination and ubiquitous occlusion conditions, this paper proposes a method to detect and locate ripe fruits based on machine vision. There are four key steps in this method including image graying and background removal, binary image optimization, true contour fragments extraction, and fruit fitting. For testing the proposed method, field experiments were conducted with tomato and citrus, and the ripe fruits in complex environments were successfully detected and located. From the detection experiments, it showed that the recognition rate for ripe fruits in the near zone of the proposed method was higher than 97.44%, the average time consumption was 0.2966 s, and the positioning error was less than 4.41%. In addition, it can be concluded from the comparative experiment that the proposed method is superior to conventional Hough transform, random Hough transform, and other methods based on deep learning in terms of detection rate, time performance and positioning accuracy. Therefore, it can be applied to picking robots for real-time detecting and locating ripe fruits. [ABSTRACT FROM AUTHOR]