目的 传统考种方法测量精度和效率难以满足现代水稻育种研究的需求，设计一种水稻谷粒图像与质量信息同步采集装置，实现水稻谷粒考种参数的自动提取。方法 采用掩膜法自动提取稻谷区域图像，根据稻谷投影面积、数量规律获取稻谷总粒数；根据空粒、实粒颖壳轮廓差异识别空粒；基于角点间距均值标定法，结合轮廓最小外接矩形法获取粒长、粒宽，结合链码法获取粒周长；采用正方形面积均值标定法结合像素累加法获取粒面积。分析摄像头高度、谷粒数量、谷粒种类、规则图形类型对谷粒性状参数提取精度的影响。结果 摄像头高度对稻谷总数、空粒数、长、宽测量精度有明显影响，稻谷种类对宽度测量精度有明显影响，规则图形类型对周长和面积测量精度有明显影响。采用本文提出方法测量总粒数、空粒数、粒长、粒宽、粒周长、粒面积的决定系数(R2)分别为0.99830、0.98780、0.99610、0.78290、0.99510和0.99998，测量的平均精度分别为99.47%、87.17%、96.55%、96.36%、98.00%和95.86%，测量效率为16.52粒/s。结论 本文所采用的稻谷谷粒考种参数自动提取方法可行，可为全自动考种机的研发提供技术参考。
Objective The measurement accuracy and efficiency of traditional seed investigation methods could not meet the needs of modern rice breeding research. A synchronous collection device of rice grain image and quality information was designed to automatically extract rice seed investigation parameters.Method The image of grain region was automatically extracted by mask method, and the total number of rice grains was obtained according to the law between rice projection area and rice quantity. Empty grains were identified according to the difference of the hull contour between empty grains and full grains. Based on the mean value calibration method of corner spacing, the grain length and width were obtained by combining the minimum circumscribed rectangle method of contour, and the grain perimeter was obtained by combining the chain code method. The square area mean calibration method and pixel accumulation method were used to obtain the grain area. The effects of camera height, grain quantity, grain type and regular graph type on the extraction accuracy of grain character parameters were analyzed.Result The camera height had a obvious impact on the measurement accuracies of total number, empty number, length and width of rice grains, the grain type had a obvious impact on the measurement accuracy of width, and the regular graph type had a obvious impact on the measurement accuracies of grain area and perimeter. The determination coefficients (R2) of total grain number, empty grain number, grain length, grain width, grain perimeter and grain area measured by the proposed method were 0.99830, 0.98780, 0.99610, 0.78290, 0.99510 and 0.99998 respectively, the average measurement accuracies were 99.47%, 87.17%, 96.55%, 96.36%, 98.00% and 95.86% respectively, and the measurement efficiency was 16.52 grains per second. Conclusion The automatic extraction method of rice seed investigation parameters used in this paper is feasible, and can provide technical references for the development of automatic seed investigation machine.