SMART: Speedy Measurement of Arabidopsis Rosette Traits

Abstract Most computer vision‐ and machine learning‐based plant phenotyping systems compute traits such as shape and size rather than the color distribution of the plant surface, even though color can provide important insights into plant physiology. Therefore, we developed Speedy Measurement of Arabidopsis Rosette Traits (SMART), an open‐source plant phenotyping pipeline that analyzes a red–green–blue (RGB) top‐view image captured by any imaging device to compute color traits as well as shape and size. SMART combines a pretrained U2‐Net machine learning model and a color clustering method to segment plants from their background and compute basic morphological traits. SMART showed a good average accuracy of 95% for morphological traits using a public benchmark dataset. Uniquely, SMART also analyzes the color of plant surfaces by calculating a normalized color difference index and comparing plant surface colors with reference colors in the L*a*b* color space, which are converted from the RGB color space. The color difference index also showed good correlation with independent measurements of the chlorophyll fluorescence parameter F v / F m (maximum quantum yield of photosystem II) ( R 2 > 0.71), chlorophyll content ( R 2 > 0.73), and leaf temperature ( R 2 > 0.76) in our experimental conditions. Therefore, we show that SMART is not only an affordable, open‐source tool for calculating morphological traits such as shape and size but also it is also useful for exploring relationships between color traits and physiological traits. SMART represents a promising new approach to low‐cost, high‐throughput phenotyping, thus benefiting the entire plant science community.