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.
Highlights
310,000 plant species grow in various regions worldwide, accounting for nearly 80% of the world’s biomass [1,2]
This study has developed a new clearing method for clearing, fixative, and dehydrating agents for their high-quality stomatal image of soybean leaves within 2 h (Figure 2)
We found that a new clearing method can apply for the adaxial side of soybean leaves as well
Summary
310,000 plant species grow in various regions worldwide, accounting for nearly 80% of the world’s biomass [1,2]. The yield and biomass of crop plants are determined mainly by the photosynthesis rate increase [3]. Most plants’ leaves have stomata associated with photosynthesis and water loss, playing an essential role in surviving the terrestrial environment. For a plant in an environment where the CO2 concentration is high, its leaves’ stomatal development tends to be suppressed to reduce stomatal density per unit area, reducing the CO2 inflow required for photosynthesis [5]. The number of stomata in leaves will be a critical factor in the tradeoff between photosynthetic carbon fixation, closely linked to photosynthesis and crop yield [3,6]. A proxy to estimate photosynthesis fitness will be the variance in the stomatal density of leaves in diverse agricultural environments such as drought [7,8,9], salinity stress [10], heat stress [11], and precipitation changes [12]
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