The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains. In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%. We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.

中文翻译：

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基于图像分析的水稻每穗粒数识别与量化

水稻每穗粒数是重要的表型性状，是品种筛选和栽培管理的重要指标。目前用于计算每穗粒数的方法是手动进行的，这使得它们劳动密集且耗时。现有的基于图像的晶粒计数方法难以分离重叠的晶粒。在这项研究中，我们旨在开发一种基于图像分析的方法来快速量化每穗的水稻粒数。我们比较了几种方法在不同图像采集设备和多种穗形对籼粳亚种水稻中的计数精度。本研究开发的线性回归模型对粳米和籼米的粒数计数精度分别大于 96% 和 97%。而且，虽然我们使用的深度学习模型比线性回归模型更耗时，但平均计数准确率大于 99%。我们开发了一种水稻粒数计数方法，可以准确计算分离穗上的粒数，并相信这种方法可以成为指导开发其他作物每穗粒数的高通量方法的巨大资产。