Pod beak and constriction play an important role in the cultivar identification of peanut. However, little research has been done in the identification of pod beak and constriction based on image processing. To identify pod beak and constriction automatically and evaluate the feasibility of image process techniques for quantifying peanut DUS traits, the identification of pod beak and constriction and quantitative analysis of DUS traits based on Freeman Chain Code were proposed. Twelve peanut varieties from Heibei and Shandong provinces (Rizhao, Weifang, Qingdao, Yantai city) were used and 50 peanut pods were randomly selected from each variety. A scanner was used to capture images of the peanut samples. Preprocessing including image segmentation, graying, enhancement and binarization were conducted to the peanut images. To make the proposed method robust to rotational change, a rotational operation was performed to make the pod in vertical direction. Then, image edge was extracted from the binary image and eight-direction freeman chain code was applied to code the edge. Approximate curvature method and local maximum method were used to detect inner corner points and exclude the pseudo corner points. Local maximum method was used to remove the adjacent corner points and keep the point with the largest curvature within a neighborhood. Pod beak and constriction were identified by their locations. Meanwhile, some other corner detection methods, such as Moravec, Harris and SUSAN algorithms, were used for comparison with the proposed method here. Finally, 3 DUS testing traits were extracted and quantified using image process techniques. Degree of pod constriction was qualified by the location of pod constriction and graded by the value of the pod constriction degree. Distinctness degree of pod beak was qualified by the curvature where the pod beak was located. The length of pod was qualified by the length of its minimal outer rectangle. To evaluate the proposed method, 600 peanut samples were selected for identification of pod beak and constriction. Results showed that the accuracy of pod beak and constriction identification based on Freeman Chain Code were 93.1% and 95.5%, respectively. The false alarm rates of pod beak and constriction identification were 7.3% and 5.8%, respectively. Comparably, the Freeman Chain Code method was better than the other three with the accuracy for pod beak identification of 53.4% with Moravec, 85.5% with Harris and 83.4% with SUSAN, and the pod constriction identification accuracy of 48.5% with Moravec, 82.3% with Harris, and 84.2% with SUSAN. Meanwhile, the processing speed was also higher (468 ms) than the others (1436, 738, 567 ms). Based on quantified DUS testing traits using Freeman Chain Code, the peanut grading accuracy could achieve 92.4% for 524 samples. The grading accuracy of the distinctness degree of pod beak was 91.7% for 436 samples. The relative error of pod length measurement was 5.8%. In sum, the Freeman Chain Code method is effective in image recognition of peanut beak and constriction and also for quantification of peanut DUS trait.%为实现花生荚果果嘴和果腰的自动识别，以及评价图像处理方法量化花生特异性（ distinctness ）、一致性（uniformity）和稳定性测试（stability）（简称DUS）性状的技术适用性，该文提出了基于Freeman编码的花生果嘴和果腰识别方法以及花生荚果DUS测试性状量化方法。首先提取荚果图像边界轮廓并进行Freeman编码，接着采用近似曲率法和局部曲率最大法确定内拐角点，然后采用位置判断法定位果嘴和果腰位置，最后对荚果缢缩程度、果嘴明显程度和荚果长度3个DUS性状进行量化。对600个花生样本的测试结果表明，该文提出的方法对果嘴和果腰的识别正确率分别为93.1%和95.5%，较其他角点检测算法在时间和准确率方面都有很大优势。同时能够有效地对相关DUS性状（荚果缢缩程度、果嘴明显程度和荚果长度）进行量化，对荚果缢缩程度和果嘴明显程度的分级准确率分别为92.4%和91.7%。图像处理具有高效、客观、低成本采集和量化花生荚果外观形状的能力，为花生外观性状的自动采集和测量提供了研究基础和依据。
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机译：Analysis on the Functions and Properties of the Preliminarily Determined Characteristics Listed in DUS Test Guideline of Tagetes L.万寿菊DUS测试指南中初步确定的性状的功能及特征分析
