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ARS Home » Midwest Area » Madison, Wisconsin » Vegetable Crops Research » Research » Publications at this Location » Publication #327379

Research Project: Cranberry Genetic Improvement and Insect Pest Management

Location: Vegetable Crops Research

Title: GiNA, an efficient and high-throughput software for horticultural phenotyping

Author
item Diaz-garcia, Luis
item Covarrubias-pazaran, Giovanny
item Schlautman, Brandon
item Zalapa, Juan

Submitted to: PLoS One
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/19/2016
Publication Date: 8/16/2016
Publication URL: http://handle.nal.usda.gov/10113/5678144
Citation: Diaz-Garcia, L., Covarrubias-Pazaran, G., Schlautman, B., Zalapa, J. 2016. GiNA, an efficient and high-throughput software for horticultural phenotyping. PLoS One. 11(8). doi: 10.1371/journal.pone.0160439.

Interpretive Summary: In crop breeding programs, massive phenotyping (trait data collection) is key for the efficient evaluation and selection of new cultivars and varieties. In these cases, multiple populations with numerous individuals are constantly phenotypically being evaluated requiring a considerable investment in time and money. The need for new approaches to massively acquire trait data will continue to increase in coming years. We developed software, called GiNA, for image-based horticultural trait data collection such as shape and color data. The GiNA image analysis framework is highly accessible and freely available to scientists and groups working in major and minor crop research programs. The application and use of this software is simple, but very helpful in terms of the massive amount of high-quality measurements that can be generated. For small fruits such as grapes, cranberries, or cherries, a picture of 40 fruits can be taken every minute (or less). Therefore, in an hour, at least 20 different parameters for 2400 fruits can be accurately measured from 60 images. The same amount of work would represent at least 20 man-hours to collect using traditional manual measurements. Although many image-based trait collection technologies are available, they are not easy-to-use and optimize, and they are not economically accessible for scientists that commonly face limitations related with massive trait data collection activities. Trait data collection using software such as GiNA can lead to an accelerated progress in crop improvement and a more efficient characterization of traits of interest for both science and industry.

Technical Abstract: Traditional methods for trait phenotyping have been a bottleneck for research in many crop species due to their intensive labor, high cost, complex implementation, lack of reproducibility and propensity to subjective bias. Recently, multiple high-throughput phenotyping platforms have been developed, but most of them are expensive, species-dependent, complex to use, and available only for major crops. To overcome such limitations, we present the open-source software GiNA, which is simple and free software for measuring horticultural traits such as shape- and color-related parameters of fruits, vegetables, and seeds. GiNA is multiplatform software available in both R and MATLAB programming languages and uses conventional images from digital cameras with minimal requirements. It can process up to 11 different horticultural morphological traits such as length, width, two-dimensional area, volume, projected skin, surface area, RGB color, among other parameters. Five-fold cross validation between manually generated and GiNA measurements for length and width in cranberry fruits were 0.970 and 0.924. In addition, the same strategy yielded prediction accuracies above 0.829 for color estimates produced from images of cranberries analyzed with GiNA compared to total anthocyanin content (TAcy) of the same fruits measured with the standard methodology of the industry. Our platform provides a scalable, easy-to-use and affordable tool for massive acquisition of phenotypic data of fruits, seeds, and vegetables.