Abstract：
CONCLUSIONS: Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).
摘要：
结论：创新，我们认为气孔检测是旋转物体检测，并提供端到端，批处理,旋转,实时气孔密度和孔径大小智能检测与识别系统,RotatedestomataNet。气孔在呼吸过程中充当空气和水蒸气的通道，蒸腾作用,和其他气体代谢，因此气孔表型对植物的生长发育具有重要意义。高通量造口的智能检测是一个关键问题。然而，当前可用的方法在面对密集和不均匀排列的气孔时通常会遇到检测错误或繁琐的操作。提出的RotatedStomataNet创新地将气孔检测视为旋转物体检测，启用端到端，实时,气孔和孔的智能表型分析。该系统是基于破坏性获取的拟南芥和玉米气孔数据集构建的，以无损方式获取的玉米气孔数据集，实现了表型的一站式自动收集，比如位置，密度,长度,以及气孔和孔的宽度，无需分步操作。该系统获取气孔和孔的准确性已经在单子叶植物和双子叶植物中得到了很好的证明，如拟南芥，大豆,小麦,和玉米。实验结果表明,该方法的预测结果与人工标注的结果一致。测试集，系统代码,和他们的用法也给出了（https://github.com/AITAhenu/RotatedStomataNet）。
