Quantitative potato tuber phenotyping by 3D imaging

Liu, Jiangang; Xu, Xiangming; Liu, Yonghuai; Rao, Zexi; Smith, Melvyn; Jin, Liping; Li, Bo

Abstract

The accurate phenotyping of the external quality attributes of potato tubers is important in potato breeding. Currently, the assessment of potato tuber shape, together with eye density and depth, are based on subjective naked eye visual evaluation. However, such a manual visual assessment makes it very difficult to reliably phenotype these and other important, more complicated, geometrical traits, such as shape uniformity. In this study, a 3D image analysis method has been developed for counting potato eyes and estimating eye depth based on an evaluation of the curvature of an acquired 3D point cloud. Six shape uniformity-related traits, together with their shape indices (SI), were measured for six potato varieties. These were collected from three field experiments designed initially to study the effects of variation in nitrogen (N), potassium (K) and compound fertilisers along with tuber mass, on all investigated external traits. We demonstrate that a 3D image analysis technique can estimate the number of potato eyes and their depth with a high degree of accuracy. In addition, three shape uniformity traits were identified as offering a better power discrimination between varieties. The preliminary experiment found potato tuber mass to significantly affect both the shape uniformity and eye count, while fertiliser treatments showed no effect on all traits except SI. However, further investigation with a larger sample size is required for confirmation.

Citation

Liu, J., Xu, X., Liu, Y., Rao, Z., Smith, M., Jin, L., & Li, B. (2021). Quantitative potato tuber phenotyping by 3D imaging. Biosystems Engineering, 210, 48-59. https://doi.org/10.1016/j.biosystemseng.2021.08.001

• Journal Article Type: Article
• Acceptance Date: Aug 4, 2021
• Online Publication Date: Aug 17, 2021
• Publication Date: Oct 1, 2021
• Deposit Date: Aug 4, 2021
• Publicly Available Date: Aug 18, 2022
• Journal: Biosystems Engineering
• Print ISSN: 1537-5110
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 210
• Pages: 48-59
• DOI: https://doi.org/10.1016/j.biosystemseng.2021.08.001
• Public URL: https://uwe-repository.worktribe.com/output/7602399
• Publisher URL: https://www.journals.elsevier.com/biosystems-engineering