RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis

Abstract

Abstract Stomata act as a pathway for air and water vapor during 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. However, current existing methods usually suffer from detection error 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 in a destructive way, and the maize stomatal data set acquired in a nondestructive way, enabling 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. And the experimental results showed that the prediction results of the method are consistent with those of manual labeled. The test sets, system code, and its usage are also given (https://github.com/AITAhenu/RotatedStomataNet).

Plant phenotyping relevance

気孔の検出と開度・密度・寸法などの表現型を自動抽出する画像解析手法を開発し、複数作物で精度検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper explicitly states that the test sets, system code, and usage instructions are publicly available at the authors' GitHub repository (https://github.com/AITAhenu/RotatedStomataNet). This is a paper-specific asset: the RotatedStomataNet system code for rotated object detection of stomata and apertures, together,