MIC-100, a new system for high-throughput phenotyping of instantaneous leaf photosynthetic rate in the field.

Photosynthesis occurs mainly in plant leaves and is a fundamental process in the global carbon cycle and in crop production. The exploitation of natural genetic variation in leaf photosynthetic capacity is a promising strategy to meet the increasing demand for crops. The present study reports the newly developed photosynthesis measurement system 'MIC-100,' with a higher throughput for measuring instantaneous photosynthetic rate in the field. MIC-100 is established based on the closed system and directly detects the CO2 absorption in the leaf chamber. The reproducibility, accuracy, and measurement throughput of MIC-100 were tested using soybean (Glycine max L. (Merr.)) and rice (Oryza sativa L.) grown under field conditions. In most cases, the coefficient of variance (CV) for repeated-measurements of the same leaf was less than 0.1. The photosynthetic rates measured with the MIC-100 model showed a significant correlation (R2 = 0.93-0.95) with rates measured by a widely used gas-exchange system. The measurement throughput of the MIC-100 is significantly greater than that of conventional open gas-exchange systems under field conditions. Although MIC-100 solely detects the instantaneous photosynthetic rate under a given environment, this study demonstrated that the MIC-100 enables the rough evaluation of leaf photosynthesis within the large-scale plant populations grown in the field.

Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection.

The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans (Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm-2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs.