Cucumber image segmentation algorithm based on rough set theory

Abstract

Abstract In this paper a segmentation algorithm for cucumber images is presented to solve the problems of target identification and position for a greenhouse cucumber‐harvesting robot. Amethod of combining the three basic colours, branch‐quantity and threshold processing is used to enhance the target im ages, increasing the contrast between background and target and reducing the information content of the colour image. The strengthened image is considered as a knowledge system. The cucumber images are then segmented by a classification decision table which is based on a knowledge‐reduction algorithm from rough set theory, choosing the image colour attribute as a condition attribute and standard colour as a decision attribute. The image noise is removed using an expansion algorithm of mathematical morphology. The experimental results show that the proposed algorithm can enhance the image of greenhouse cucumbers and segment the profile of the cucumber image effectively. There is less noise in the image after segmentation.