Automated Stomatal Detection and Enumeration Using Machine Vision Techniques

Stomata represent pivotal sites for water exchange in plants, orchestrating the vital processes of gas exchange, photosynthesis, and transpiration. Guarded by specialized cells, stomata regulate the rate of transpiration by opening and closing. The status of stomata, whether open or closed, serves as an indicator of a plant's health. Consequently, the quantification of stomata assumes great significance in scientific research, facilitating the study of stomatal dynamics, density, and distribution on leaf surfaces through diverse sampling methodologies. Both manual stomatal observation and automated detection techniques play crucial roles in this pursuit, impacting fields such as taxonomy, biology, physiology, and eco-physiology. In this study, we have designed a machine vision system utilizing an affordable USB microscope to accurately count stomata on live leaves of the Oyster plant (Tradescantia spathacea Stearn). Functional tests of the developed system reveal its capability to swiftly and precisely enumerate stomata in real-time, achieving an accuracy rate of 68.75%. This innovative approach offers several advantages over conventional counting methods, including increased speed, reduced labor demands, cost-effectiveness, and the capacity to count stomata continuously and in large quantities over extended durations. The developed system empowers scientists with a high-speed, accurate, and efficient means of obtaining stomata count data, while also serving as an educational tool for elementary and secondary students to explore plant morphology and various objects in diverse forms.