Identifying and quantifying the number and size of stomata on leaf surfaces is useful for a wide range of plant ecophysiological studies, specifically those related to water-use efficiency of different plant species or agricultural crops. The time-consuming nature of manually counting and measuring stomata have limited the utility of manual methods for large-scale precision agriculture applications. A deep learning segmentation network was developed to automate the analysis of stomatal density and size and to distinguish between open and closed stomata using citrus trees grafted on different rootstocks as a model system. A novel method was developed utilizing the Mask-RCNN algorithm, which allows identification, quantification, and characterization of stomata from leaf epidermal peel microscopic images with an accuracy of up to 99%. Moreover, this method permits the differentiation of open and closed stomata with 98% precision and measurement of individual stomata size. In the citrus model system, significant differences in the size and density of stomata and diurnal regulation patterns were detected that were associated with the rootstock cultivar on which the trees were grafted. Nearly 9000 individual stomata were analyzed, which would have been impractical using manual methods. The novel automated method presented here is not only accurate, but also rapid and low-cost, and can be applied to a variety of crop and non-crop plant species.

中文翻译：

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利用机器视觉和人工智能确定柑橘树的叶气孔特性

识别和量化叶表面气孔的数量和大小对于广泛的植物生态生理学研究非常有用，特别是与不同植物物种或农作物的水分利用效率相关的研究。手动计数和测量气孔的耗时性质限制了手动方法在大规模精准农业应用中的实用性。开发了一个深度学习分割网络，以自动分析气孔密度和大小，并使用嫁接在不同砧木上的柑橘树作为模型系统区分开放和封闭的气孔。利用 Mask-RCNN 算法开发了一种新方法，该方法可以识别、量化和表征叶表皮显微图像中的气孔，准确率高达 99%。而且，这种方法允许以 98% 的精度区分开放和封闭的气孔，并可以测量单个气孔的大小。在柑橘模型系统中，检测到与嫁接树木的砧木品种相关的气孔大小和密度以及昼夜调节模式的显着差异。分析了近 9000 个单独的气孔，使用手动方法是不切实际的。这里介绍的新型自动化方法不仅准确，而且快速且成本低，可应用于多种作物和非作物植物物种。检测到与嫁接树木的砧木品种有关的气孔大小和密度以及昼夜调节模式的显着差异。分析了近 9000 个单独的气孔，使用手动方法是不切实际的。这里介绍的新型自动化方法不仅准确，而且快速且成本低，可应用于多种作物和非作物植物物种。检测到与嫁接树木的砧木品种有关的气孔大小和密度以及昼夜调节模式的显着差异。分析了近 9000 个单独的气孔，使用手动方法是不切实际的。这里介绍的新型自动化方法不仅准确，而且快速且成本低，可应用于多种作物和非作物植物物种。