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.

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“…Stomata were imaged using light microscopy, and counted in three randomly selected fields of view per leaf sample. Stomatal aperture and stomatal index measurements were conducted as described previously (Zhu et al ., 2021; Sultana et al ., 2021).…”

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“…Counting of stomata and pavement cells was done using the “cell-counter” function of ImageJ. Confocal imaging was done on the 4 th true leaf after leaves were exposed to a fixing solution of 1:5 35% formaldehyde: ethanol for <2 hours (Sultana et al, 2021). Leaves were then rinsed and stained with 10 μg/mL propidium iodide (PI) for a minimum of 5 minutes before leaves were mounted on slides with water to be imaged using a Leica Confocal Microscope (Raissig 2017).…”

“…Counting of stomata and pavement cells was done using the "cell-counter" function of ImageJ. Confocal imaging was done on the 4 th true leaf after leaves were exposed to a fixing solution of 1:5 35% formaldehyde: ethanol for <2 hours (Sultana et al, 2021).…”