Image-based plant stornata phenotyping

We propose in this paper a fully automatic approach for image-based plant stornata phenotyping. Given a microscopic image of a plant leaf surface, our goal is to automatically detect stornata cells and measure their morphological and structural features, such as stornata opening length and width, and size of the guard cells. The main challenge in developing such tool is the lack of contrast between the stornata cell region and its surrounding background. Our approach uses template matching to detect individual stornata cells and local analysis to measure stornata features within the detected stornata regions. It is fully automatic and computationally efficient. Thus, it will enable plant biologists to perform large scale analysis of stornata morphology, which in turn will help in developing understanding and controlling plant´s response to various environmental stresses (e.g. drought and soil salinity).