A method for phenotyping roots of large plants. Issue 1 (6th May 2022)

• Record Type: Journal Article
• Authors: Rinehart, Brian; Poffenbarger, Hanna; Lau, Daniel; McNear, Dave
• Is Part Of: Plant phenome journal. Volume 5:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-06
• Subjects: Phenotype -- Periodicals; Plant genetics -- Periodicals; Periodicals; 581.35
• DOI: 10.1002/ppj2.20041
• Languages: English
• ISSNs: 2578-2703

Abstract

Abstract: 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. Core Ideas: Phenotyping of large intact root systems is needed to accelerate crop improvement. Advancements in image segmentation have removed the requirement of uniform backlighting. Image quality and resolution of our method is equal to that of scanner‐based methods. Accurate trait data can be extracted from images taken with relatively inexpensive equipment.