Robust Weed Recognition Using Blur Moment Invariants

Abstract

Image motion blur and defocus blur often occur when there is a relative motion between the imaging camera and the detected object. These two blurs will degrade the image quality and will also decrease the subsequent pattern recognition accuracy. In this paper, we propose a robust weed recognition scheme using the low quality color weed images with the above-mentioned image blurs. The proposed scheme consists of three steps. First, image matte is used to segment the soil and the plant. Second, the image-moment-based blur invariant features are calculated. Third, weed recognition is performed by using the computed Euclidean distance based on the moment invariants. We have experimentally proved that the effective use of image blur information improves the recognition accuracy of camera-captured weeds.