Tomato Analyzer-color Test: A New Tool for Efficient Digital Phenotyping

Measuring plant characteristics via image analysis has the potential to increase the objectivity of phenotypic evaluations, provides data amenable to quantitative analysis, and is compatible with databases that aim to combine phenotypic and genotypic data. We describe a new tool, which is implemented in the Tomato Analyzer (TA) software application, called Color Test (TACT). This tool allows for accurate quantification of color and color uniformity, and allows scanning devices to be calibrated using color standards. To test the accuracy and precision of TACT, we measured internal fruit color of tomato (Solanum lycopersicum L.) with a colorimeter and from scanned images. We show high correlations (r > 0.96) and linearity of L*, a*, and b* values obtained with TACT and the colorimeter.We estimated genotypic variances associated with color parameters and show that the proportion of total phenotypic variance attributed to genotype for color and color uniformity measured with TACT was significantly higher than estimates obtained from the colorimeter. Genotypic variance nearly doubled for all color and color uniformity traits when collecting data with TACT. This digital phenotyping technique can also be applied to the characterization of color in other fruit and vegetable crops. Digital phenotyping aims to accurately describe a trait based on analysis of electronic images. Computer-based analysis of objects from digital images has the potential to increase the objectivity of data collection while reducing subjective characterization that is typically prone to bias. There are a number of computer image acquisition and analysis techniques for color in foods such as apple [Malus ·domestica Borkh (Leemans et al., 2002; Li et al., 2002)], banana [Musa cavendishii L. (Mendoza and Aguilera, 2004)], chicory [Cichorium intybus L. (Zhang et al., 2003)], as well as seed analysis (Granitto et al., 2002; Sako et al., 2001; Shahin and Symons, 2001) and meat (O’Sullivan et al., 2003; Tan, 2004). Color image analysis is also prevalent in floricultural crops such as lisianthus [Eustoma grandiflorum Grise. (Yoshioka et al., 2006)], and begonia [Begonia ·tuberhybrida Voss. (Lootens et al., 2007)]. Digital color analysis is also performed in plant pathology to quantify lesions on diseased leaves (Kwack et al., 2005). Objective and systematic descriptions, trait ontologies, are being developed in the plant sciences for database retrieval and archiving (reviewed in Brewer et al., 2006; Ilic et al., 2007). This trend is stimulated, in part, by a desire to link trait descriptions to the growing databases of sequence information. Tomato has become the prominent model horticultural crop for studies in genetics and genomic sciences. With extensive resources, including 357,477 expressed sequence tags (National Center for Biotechnology Information, 2008) and a genome sequencing project focused on euchromatin (Mueller et al., 2005), there is promise for research that seeks to integrate emerging sequence resources with phenotypic variance. Fulfilling this promise will require extensive data for the traits studied. Immortal populations (e.g., recombinant inbred populations and inbred backcross populations) consist of nearly homozygous lines that preserve the genetic integrity of mapping populations. These populations serve as a resource and allow for replication of experiments and extensive analyses from different laboratories. Phenotypic and molecular characterization of such populations can be stored in public databases for use by other researchers. The Tomato Analyzer (TA) software application was developed to facilitate the collecting and sharing of data related to fruit size and shape and the identification of genes that contribute to quantitative variation in morphology (Brewer et al., 2006). We describe a new module implemented in TA that can accurately collect objective data for color from digital images. Measuring color from digital images requires standardization and interpretation because digital devices use a color space that is not standardized, is nonlinear, and may vary between hardware devices and software applications. In the Red Green Blue (RGB) color space, each pixel is represented in the computer or interface hardware as values of red, green, and blue. In contrast, color spaces such as CIELab were designed to approximate human perception of color [Commission Internationale de l’Éclairage (CIE), 1978]. CIELab color space is a Received for publication 28 Nov. 2007. Accepted for publication 8 Apr. 2008. We thank David Sullivan (Department of Mathematics and Computer Science, College of Wooster, OH) for his help in designing and optimizing the software application for TACT. We acknowledge Bert Bishop (Statistics and Computing Services, OARDC/OSU, Wooster, OH) for his suggestions on the analysis of the IBC population. We appreciate the help from Troy Aldrich for establishing