Abstract:
Abstract: Maize ear morphological characteristics have important applications in breeding, germplasm, and cultivation areas, subject to the extent of technology development in relevant areas, but the approach of surveying morphological characteristics is not highly automatic. In this paper, we present a new machine vision based method and a supporting device for maize ear morphological characteristic surveying. First, the maize ear was placed on a rotating component, which rotates the maize ear in a fixed angle interval in order to capture 16 images more or less. A preprocess was carried out of maize ear image sequences to remove the image background, and the remaining part of the maize ear image was passed to the next process. The SIFT (Scale Invariant Feature Transform) was used to extract image feature points, and the feature points in the neighboring images could be matched up according to SIFT feature points. The relative motion between the two images could be described by a homography, and an overdetermined equations composed of matching points and homography make specific values of homography available. Mismatched feature points will reduce the accuracy of the homography equation solution dramatically. We adopted a RANSAC (random sample consensus) method to remove the outlier of the matching points during the homography solving process. Secondly, according to the motion described by homography, the first image and the next image are registered to the same coordinate system, using the dynamic programming method to find the seam-line in the two images, cutting the redundancy region in the two images along the seam-line. Since the exposure of the two images had certain differences which led to image brightness near seam-line being slightly different, a weighted Gaussian filter was imposed on both sides of the stitching image to eliminate exposure difference. Finally, the fusion image according to the order in sequence generated the ear panorama, row number, number in a row, kernel number, and other parameters were extracted by processing the maize ear panorama. The experimental results showed that: there is no significant difference between the method proposed by this paper and manual measurement, and the method proposed can greatly strengthen the automation of the maize ear traits investigation.