基于油菜角果长度图像识别的每角粒数测试方法

油菜育种考种和产量估测都需要测试油菜每角果籽粒数，现阶段油菜每角粒数仍然通过人工拨荚计数籽粒数量的方式获取，人工计数方式费时费力效率低下，已经远远不能满足现代化育种和产量估测需要。针对油菜每角粒数依靠人工计数的不足，该研究提出一种基于油菜角果长度图像识别的每角粒数测试方法。通过人工测量油菜角果长度和每角粒数，建立油菜角果长度与每角粒数之间的关系模型，通过扫描仪获取平铺状态下的油菜角果图像，角果图像经预处理和细化处理后利用击中击不中变换对角果的端点和交点进行检测，使用基于密度的具有噪声的空间聚类DBSCAN（Density-Based Spatial Clustering of Applications with Noise）算法去除多余的交点，对端点进行分类和配对并求出对应角果的长度，将角果长度代入油菜角果长度与每角粒数之间的相关性模型中得到每角粒数。结果表明，3个品种的油菜角果长度平均图像识别准确度为97.25 %，每角粒数平均预测准确度为83.87%。该方法实现了油菜每角粒数自动识别计数功能，提高了油菜每角粒数计数效率，对油菜育种考种和产量预测都具有重要意义。

Testing method for the seed number per silique of oilrape based on recognizing the silique length images

The slique is one of the most important organs to store the energy matter in an oilrape plant, a typical oil crop in China. It is necessary to measure the seed number per silique of oilrape for the breeding traits, even for the estimation of rape yields. But the current manual counting cannot fully meet the harsh requirement of modern breeding, due to the time-consuming, labor-intensive, and less accuracy for the seed number per silique of oilrape, particularly by manually breaking the pod of rape. In this study, Image recognition of the silique length was proposed to count the seed number per silique of oilrape. Three varieties of oilrape were also used as the experiment materials. The siliques were first selected from the upper, middle and lower parts of rape plants. After that, a digital calliper was utilized to measure the length of silique pod without considering the fruit stalk and beak part. A correlation model was then established for the length of silique and seed number per silique. The images of siliques were also captured by a scanner in the tiled state. The image preprocessing included the gray scale, binarization, hole filling, and the removal of small areas. Meanwhile, the fruit stalk and fruit beak of silique were removed from the images of siliques using the opening operation of mathematical morphology. After that, the images were segmented to the sub-images of siliques with the single connected domain. Each sub-image of siliques was then thinned to a line with one pixel. The endpoint and intersection point of siliques images were finally obtained using hit-or-miss transformation. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) was also used to remove the redundant intersection points. The endpoints of silique images were classified and paired to recognize the single silique. As such, the number of image pixels was obtained for the length of a single silique. The actual length of silique was calculated by a relative formula between the single pixel and actual length. Correspondingly, the seed number per silique was finally calculated from the images using the correlation model between the actual length of silique and seed number per silique. The determined coefficient for the correlation model between the length of silique and seed number per silique in the three varieties were 0.891, 0.881, 0.887, respectively, indicating a better performance of the estimation on the seed number per silique using the length of the silique. Furthermore, the average recognition accuracy was 97.25% for the length of silique, and the average prediction accuracy of seed number per silique was 83.87% using image processing. Consequently, image processing can be widely expected to realize the automatic recognition and counting of the seed number per silique for higher efficiency of rape production in modern agriculture.