Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, School of Mathematics and Statistics, Henan University, Kaifeng, 475004, Henan, China.
School of Automation, Central South University, Changsha, 410000, Hunan, China.
Plant Cell Rep. 2024 Apr 23;43(5):126. doi: 10.1007/s00299-024-03149-3.
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 ).
创新性地，我们将气孔检测视为旋转目标检测，并提供了一个端到端、批量、旋转、实时的气孔密度和孔径大小智能检测和识别系统，RotatedeStomataNet。气孔在呼吸、蒸腾和其他气体代谢过程中充当空气和水蒸气的通道，因此气孔表型对于植物的生长和发育很重要。高通量气孔的智能检测是一个关键问题。然而，目前可用的方法在面对密集和不均匀排列的气孔时通常会出现检测错误或繁琐的操作。所提出的 RotatedStomataNet 创新性地将气孔检测视为旋转目标检测，实现了气孔和孔径的端到端、实时和智能表型分析。该系统基于破坏性获取的拟南芥和玉米气孔数据集以及非破坏性获取的玉米气孔数据集构建，能够一站式自动收集气孔和孔径的表型，如位置、密度、长度和宽度，而无需分步操作。该系统在单子叶植物和双子叶植物（如拟南芥、大豆、小麦和玉米）中获取气孔和孔径的准确性得到了很好的证明。实验结果表明，该方法的预测结果与手动标记的结果一致。还提供了测试集、系统代码及其使用方法（https://github.com/AITAhenu/RotatedStomataNet）。
