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.
Highlights
Ensuring and increasing food security for a growing global population faced with uncertain environmental changes is one of the main challenges of agricultural research in the 21st century (Tilman et al, 2011; Hickey et al, 2019; Lenaerts et al, 2019)
A total of 30,933 single nucleotide polymorphism (SNP) markers assayed from the Illumina Brassica Infinium SNP array were used to evaluate the genetic diversity among a wide collection of Brassica napus lines (Supplementary Table 3.1)
The founder lines of the SKBnNAM were selected to be well distributed among the genotyped spring-type accessions to capture most of the available variation (Figure 5)
Summary
Ensuring and increasing food security for a growing global population faced with uncertain environmental changes is one of the main challenges of agricultural research in the 21st century (Tilman et al, 2011; Hickey et al, 2019; Lenaerts et al, 2019). Even though technological advances, such as genomic selection and doubled haploid technology, have resulted in substantial acceleration of the breeding process, development of new high yield, pest and disease resistant, and climatesmart crop varieties is still hampered by several factors. These include long generation times and time-consuming steps such as phenotypic evaluation of large populations (Hickey et al, 2019). N99-508, this panel includes seven Canadian adapted, five Australian, 13 European, 18 Asian as well as seven other (e.g., Argentinian and yellow-seeded) lines (Supplementary Table 3.2), which capture the genetic spectrum of spring-type B. napus (Figures 5, 6). Number of down-sampled observations (total/training data/testing data) 266/212/54
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