Seeing the wood for the trees: hyperspectral imaging for high throughput QTL detection in raspberry, a perennial crop species

Abstract

Physiological and physical traits are excellent indicators of many crop characteristics, but precise phenotyping of these traits is time consuming and, therefore, limits progress in crop breeding and the speed of crop monitoring. Hyperspectral imaging offers an opportunity to overcome these barriers as a technique for high throughput field measurements. Using a recently developed hyperspectral imaging platform devised for plantations of the perennial crop raspberry, this study aimed to further develop the tool and test its capacity as an innovative approach for high throughput field phenotyping, data collection and analysis. Hyperspectral imaging and visual crop assessments were carried out over two growing seasons in a field-grown raspberry mapping population, and data were subject to Quantitative Trait Loci (QTL) analysis. The findings show that reflectance intensity at multiple wavelengths can be linked to known genetic markers in raspberry, and many of these 'spectral traits' are expressed consistently through the growing season and between years, for example spectral ratio 719 nm / 691 nm shows up consistently as a QTL on LG4. Spectral traits were identified that co-located with previously mapped physical traits, such as 719 nm / 691 nm and cane density. The study indicates that hyperspectral imaging can be used as an innovative approach for high throughput field phenotyping of raspberry and could be transferred readily to other perennial crops. Our approach provides a pipeline for automated field data collection and analysis that can be used for rapid QTL detection of spectral traits.