A Deep Learning approach for precision phenotyping of Corn Stomata

On the epidermal surface of plants, a kidney-shaped organ called stomata plays an important role in plant health under drought conditions. These stomata which resembles to be like a pore, opens and closes during transpiration. The lower the stomatal transpiration in drought, plants can escape drought when the rate of photosynthesis is balanced. A greater number of open stomata indicates that plants are experiencing drought. To assess stomata response, one must derive the pore aperture ratio. The lower the pore aperture ratio of plants, the more closed stomata are in response to drought, consequently decreasing transpiration. Here we show the development and implementation of StomaDetectv1, a novel deep learning model for non-destructive, high-throughput phenotyping of corn stomata, utilizing a custom Faster R-CNN architecture. StomaDetectv1 achieves an Average Precision of 84.988% for closed stomata areas, underpinning its efficacy in identifying variations in stomatal traits. The model was adept at assessing stomatal density and aperture ratios, essential for quantifying drought resilience. This work underscores the significance of integrating imaging techniques and deep learning for precision phenotyping, offering a scalable solution for monitoring plant circadian rhythm, and aiding in the breeding of drought-resistant crops. By furnishing breeders and geneticists with detailed insights into stomatal behavior, our approach catalyzes the development of corn varieties optimized for water use efficiency and yield under drought conditions, thereby advancing agricultural practices to combat climate challenges.