A precise count of flower number per inflorescence is essential to characterize the reproductive behavior of a vine. Previous efforts to automatize this process by image-based technologies have failed in the development of a universal system that can be applied to multiple grapevine cultivars, or they have been tested in a set of inflorescences of narrow morphological diversity. Here, we have developed an alternative general method in the open-source platform Fiji for the nondestructive counting of visible flowers in red-green-blue (RGB) images, considering inflorescences from 45 different grapevine genotypes from three progenies segregating for inflorescence morphology. The algorithm, based on the segmentation of the image into regions of interest according to their color and morphology, provided counting results highly correlated to manual ones (R2 = 0.91). Similar results were obtained when validating this tool in an external data set of 400 images of four grapevine cultivars. Counting values were used for actual flower number estimation by linear modeling using a subset of 45 images, considering a flower density factor to reduce the adverse effect of the variable number of hidden flowers. Our approach allowed the estimation of flower number with satisfactory results, providing useful information for grapevine breeding and research.

中文翻译：

---

使用 RGB 图像自动评估葡萄花序中的花数

每个花序的花数的精确计数对于描述葡萄树的繁殖行为至关重要。以前通过基于图像的技术使这一过程自动化的努力未能开发出可应用于多种葡萄栽培品种的通用系统，或者它们已经在一组形态多样性狭窄的花序中进行了测试。在这里，我们在开源平台斐济开发了一种替代通用方法，用于对红绿蓝 (RGB) 图像中的可见花进行无损计数，考虑到来自三个后代的 45 种不同葡萄基因型的花序，这些花序分离花序形态。该算法基于根据图像的颜色和形态将图像分割为感兴趣的区域，提供与手动计数高度相关的计数结果（R2 = 0.91）。在四个葡萄品种的 400 张图像的外部数据集中验证此工具时，获得了类似的结果。计数值用于通过线性建模使用 45 个图像的子集进行实际花数估计，考虑到花密度因子以减少隐藏花的可变数量的不利影响。我们的方法允许估计花数并获得令人满意的结果，为葡萄育种和研究提供有用的信息。考虑花密度因子以减少隐藏花数量可变的不利影响。我们的方法允许估计花数并获得令人满意的结果，为葡萄育种和研究提供有用的信息。考虑花密度因子以减少隐藏花数量可变的不利影响。我们的方法允许估计花数并获得令人满意的结果，为葡萄育种和研究提供有用的信息。