Stomata are vital for regulating water and carbon dioxide in plants, affecting photosynthesis. Traditional stomata analysis was manual and error-prone, but deep learning (DL) methods, such as DCNN, have been introduced for enhanced detection and measurement. However, these advanced techniques still face challenges in accurately calculating stomatal traits due to the random orientation of stomata, requiring additional image processing.
A deep learning–based method for high-precision stomata detection and conductance analysis
