Abstract
Visual assessment of colour-based traits plays a key role within field-crop breeding programmes, though the process is subjective and time-consuming. Digital image analysis has previously been investigated as an objective alternative to visual assessment for a limited number of traits, showing suitability and slight improvement to throughput over visual assessment. However, easily adoptable, field-based high-throughput methods are still lacking. The aim of the current study was to produce a high-throughput digital imaging and analysis pipeline for the assessment of colour-based traits within a wheat breeding programme. This was achieved through the steps of (i) a proof-of-concept study demonstrating basic image analysis methods in a greenhouse, (ii) application of these methods to field trials using hand-held imaging, and (iii) developing a field-based high-throughput imaging infrastructure for data collection. The proof of concept study showed a strong correlation (r = 0.95) between visual and digital assessments of wheat physiological yellowing (PY) in a greenhouse environment, with both scores having similar heritability (H2 = 0.85 and 0.76, respectively). Digital assessment of hand-held field images showed strong correlations to visual scores for PY (r = 0.61 and 0.78), senescence (r = 0.74 and 0.75) and Septoria tritici blotch (STB; r = 0.76), with greater heritability of digital scores, excluding STB. Development of the high-throughput imaging infrastructure allowed for images of field plots to be collected at a rate of 7,400 plots per hour. Images of an advanced breeding trial collected with this system were analysed for canopy cover at two time-points, with digital scores correlating strongly to visual scores (r = 0.88 and 0.86) and having similar or greater heritability. This study details how high-throughput digital phenotyping can be applied to colour-based traits within field trials of a wheat breeding programme. It discusses the logistics of implementing such systems with minimal disruption to the programme, provides a detailed methodology for the basic image analysis methods utilized, and has potential for application to other field-crop breeding or research programmes.
Highlights
Visual assessment of traits within field trials is subjective and laborious
Digital scores correlated strongly (r = 0.95) with visual scores assessed from individual plants (Figure 3), with genetic and residual correlations being strong (Table 2)
The image analysis methods proposed in the current study offer a low-budget, open-source alternative to the controlled environment systems described above, and are suitable for the collection of digital scores comparable to visual scores of colour-based traits
Summary
Visual assessment of traits within field trials is subjective and laborious. it is an essential process for plant breeders who wish to observe the phenotype of material within their programme and determine genotype-by-environment effects. RGB cameras, in particular, have a long history with field phenotyping and in a number of studies have been effective in estimating canopy cover of field crops (Lukina et al, 1999; Casadesús et al, 2007; Liu and Pattey, 2010; Mullan and Reynolds, 2010). The use of RGB cameras as a phenotyping tool has focused on digital images to estimate canopy cover or as an alternative to NDVI (Casadesús et al, 2007; Morgounov et al, 2014) They have been used to a lesser extent to assess senescence (Adamsen et al, 1999; Hafsi et al, 2000), crop nitrogen content (Li et al, 2010), early vigour (Kipp et al, 2014) and soil water evaporation (Mullan and Reynolds, 2010). Image analysis techniques used to asses this range of traits have the potential to be applied to other colour-based traits, such as disease assessment, which may provide wheat breeders with an objective system of assessment for specific traits within their breeding programme
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
