Segmentation of Arabidopsis thaliana Using Segment-Anything

Speedy Measurement of Arabidopsis Traits (SMART) is a high-throughput phenotyping pipeline that processes images to observe leaf-specific traits. The current pipeline utilizes a k-means clustering segmentation method. This method has limitations in some cases where leaves display dynamic change in color and shape. Segment-Anything, a new foundational model in segmentation, offers a new method of high-resolution segmentation of complex geometries present in plant systems. Here we present a new method for segmentation of plant using Segment-Anything and Grounded-Dino. A method of obtaining individual leaves was implemented to describe the advantages and limitations of the prompt-based method of deep learning models. An analysis of the segmentation results from Segment-Anything demonstrates that this is a powerful method for providing statistically valuable data for biological insights into novel plant traits under nutrient stress.¹