Non-Destructive Phenotyping of Duckweed Using Segment Anything Model

Plant phenotyping using image analysis is gaining attention due to its potential to enhance agricultural research and productivity. Among the various species, Lemna minor, a type of duckweed, shows promise for applications such as livestock feed, biofuel production, wastewater nutrient recovery, and ecotoxicity testing. Given these benefits, accessible and efficient phenotyping methods are essential for evaluating its health and growth. This study presents a non-destructive, cost-effective, and automated phenotyping method for Lemna minor. Images captured using standard smartphone cameras are processed through an advanced analysis pipeline, with the Segment Anything Model (SAM) as a central component. This segmentation model, trained on diverse datasets, enhances adaptability across various plant species. By combining SAM with color thresholding, leaves are segmented, background is removed and features like leaf count and RGB values are extracted. These features were analyzed for their correlation with chlorophyll content, a key indicator of plant health. The results demonstrate strong correlations between RGB values and chlorophyll content and provide informative leaf count estimations. This approach is generalizable to other plant species and offers a scalable alternative to traditional destructive phenotyping methods, paving the way for broader applications in plant science.