Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

Abstract

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

The evaluation of secondary seed dormancy potentials of spring Brassica napus L. genotypes and the relationship with seed germination, vigor, and seed quality traits

“…The spring B. napus nested association mapping (NAM) population was developed as a genomic resource designed to represent the diversity in the species and study complex traits such as secondary seed dormancy (Ebersbach et al., 2022). The spring NAM parental population is comprised of 51 spring B. napus genotypes that were selected based on genotypic and phenotypic diversity.…”

Harnessing genome prediction inBrassica napusthrough a nested association mapping population

“…The B. napus Nested Association Mapping (SKBnNAM) founder panel of 51 lines was described in Ebersbach et al, (2021). The founders were each crossed to a common parent (NAM 0) resulting in 50 different F1 combinations, which through five generations of single seed descent generated the SKBnNAM recombinant inbred lines (RILs) (Figure S1).…”

PlantServation: time-series phenotyping using machine learning revealed seasonal pigment fluctuation in diploid and polyploidArabidopsis

“…Furthermore, PlantServation can be exploited for other studies, including the screening of crops, where trait scoring at an individual plant or a finer level is informative. For example, the drought and iron deficiency responses or disease resistance quantified in previous studies can be monitored for a longer period [63][64][65] . Furthermore, although it was not the focus of the current study, the combination of our image acquisition system and the image analysis pipeline could also detect differences in morphological features among plants, as observed in the segmentation results (Fig.…”