Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning.

Abstract

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation-a 1:1 ( v / v ) mixture of 95% ethanol and NaOCl (6-14%)-produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

Plant phenotyping relevance

ダイズ葉の気孔画像取得法と、気孔を自動検出・解析する深層学習手法を開発・評価しており、植物形質取得が研究の中心である。

Code and data availability

The paper's soybean stomatal phenotype dataset (Table S2, manual vs. automatic stomatal density for 386 accessions) is publicly available via the MDPI supplement, and the trained YOLOv5 stomata-detection model is publicly served through the authors' web application. The 183-image training dataset and analysis code have