Plant Leaves Region Segmentation in Cluttered and Occluded Images Using Perceptual Color Space and K-means-Derived Threshold with Set Theory

Abstract

Presence of clutters and occluding objects within agricultural farm environments challenges accurate segmentation of plant leaves, a prerequisite for an effective machine-vision-based automation of agricultural tasks. In this paper, we propose a plant leaves segmentation method that can be integrated into vision-based robotic harvester and quality inspection systems. The proposed method combines the discriminatory power of color-based technique with the simplicity and computational efficiency of threshold-based technique. Clutters and occluding objects are eliminated by infinitesimal angular displacement of the threshold image, followed by the application of set theory. Performance evaluation shows that the proposed method demonstrate strong robust features and computational efficiency.