Linking root traits to plant functions can enable crop improvement for yield and ecosystem functions. However, plant breeding efforts targeting belowground traits are limited by appropriate phenotyping methods for large root systems. While advances have been made allowing for imaging large in situ root systems, many of these methods are inaccessible due to expensive technology requirements. The aim of this work was to develop a plant phenotyping platform and analysis method suitable for assessing root traits of large, intact root systems. With the use of a purpose-built imaging table and automated photo capture system, machine learning-based image segmentation, and off-the-shelf trait analysis software, the developed method yielded results of comparable accuracy to commercial root scanning platforms without requiring access to prohibitively expensive equipment. This methodology enables root studies to move beyond the size limitations of scanner-based methods, integrate whole-system traits like root depth distribution, and save time on root image capture.
Bibliographical noteFunding Information:
Funding was provided by USDA NIFA award number 2019‐67019‐29401, a cooperative agreement with the Kentucky Agricultural Experiment Station (KY006117), and the Department of Plant and Soil Sciences, University of Kentucky. We thank Corteva Agriscience™ for providing Era hybrid seed. We would also like to acknowledge the assistance of Osei Jordan, Joe Kupper, Walter Rhodus, Laura Harris, and Katie Jacobs with setting up and sampling in the greenhouse, and the assistance of Lucas Pecci Canisares and Travis Banet with root system imaging. We also thank Dr. Carlos Messina for his helpful feedback.
© 2022 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America.
ASJC Scopus subject areas
- Agronomy and Crop Science
- Plant Science