CAJ | 학술논문

为了实现基于机器视觉方法的玉米单倍体种子识别,该文研究了一种玉米单倍体种子胚部特征提取及动态识别方法.采用一种基于 B 通道平均像素值的胚部特征提取方法,提取了具有 Navajo 标记的玉米种子的胚部图像,基于此在 RGB 颜色空间内提取了样本的 Navajo 标记图像,从而得到一套玉米单倍体种子快速识别 RGB 组合算法.在玉米分选试验台上进行了动态分选试验.试验结果表明,该算法对 LC09124-UH400品种玉米单倍体的识别正确率为98.04%,对杂合体的识别正确率为94.44%.该文提出的玉米单倍体种子 RGB 组合快速识别算法与玉米分选试验台结合形成的动态分选系统,有助于实现玉米单倍体种子的自动化分选.
위료실현기우궤기시각방법적옥미단배체충자식별,해문연구료일충옥미단배체충자배부특정제취급동태식별방법.채용일충기우 B 통도평균상소치적배부특정제취방법,제취료구유 Navajo 표기적옥미충자적배부도상,기우차재 RGB 안색공간내제취료양본적 Navajo 표기도상,종이득도일투옥미단배체충자쾌속식별 RGB 조합산법.재옥미분선시험태상진행료동태분선시험.시험결과표명,해산법대 LC09124-UH400품충옥미단배체적식별정학솔위98.04%,대잡합체적식별정학솔위94.44%.해문제출적옥미단배체충자 RGB 조합쾌속식별산법여옥미분선시험태결합형성적동태분선계통,유조우실현옥미단배체충자적자동화분선.
Haploid breeding is an efficient new way in breeding, but the natural generating possibility for maize haploid seeds is too low. However, haploid seeds are generally sorted by hand, which reduces the sorting efficiency. A sorting way of haploid that is labour-saving, timesaving and with high accuracy should be found. Nowadays, machine vision technology has been more improved and widely applied to the processing, identification, and classification of agricultural products, which makes it possible to realize the automatic sorting for maize haploid seeds based on machine vision. Maize seeds with genetic marker gene within a same variety are mainly classified into haploid and hybrid according to the distribution of Navajo genetic markers on different parts of each seed, especially the embryo. So the characteristics of maize embryo are important to distinguish between haploid and hybrid seeds. In this paper, the embryo feature extraction and dynamic recognition method for maize seeds with genetic markers was studied. An image segmentation method to extract the characteristics of embryo referring to the average pixel values of B channel was proposed. And the embryo images of the maize seeds with Navajo genetic markers were extracted. The Navajo genetic markers in the acquired embryo region were extracted in RGB color space, and the number of Navajo pixels for each seed was counted to judge which classification it belonged to. A rapid recognition algorithm of maize haploid seeds was obtained after embryo feature extraction and Navajo marker extraction. The dynamic sorting test for LC9124-UH400 maize seeds was performed on the maize haploid sorting platform, and the result of the test showed that the recognition rate for haploid seeds was 98.04%, and that for hybrid seeds was 94.44%. The main factors that affected the recognition result in the test were analyzed. A dynamic sorting system for maize haploid seeds including the algorithm and the maize haploid sorting platform has been built up, which has a helpful significance for the realization of maize haploid automatic sorting.