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

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.

Fast segmentation of anterior segment optical coherence tomography images using graph cut

Background:Optical coherence tomography(OCT)is a non-invasive imaging system that can be used to obtain images of the anterior segment.Automatic segmentation of these images will enable them to be used to construct patient specific biomechanical models of the human eye.These models could be used to help with treatment planning and diagnosis of patients.Methods:A novel graph cut technique using regional and shape terms was developed.It was evaluated by segmenting 39 OCT images of the anterior segment.The results of this were compared with manual segmentation and a previously reported level set segmentation technique.Three different comparison techniques were used:Dice’s similarity coefficient(DSC),mean unsigned surface positioning error(MSPE),and 95%Hausdorff distance(HD).A paired t-test was used to compare the results of different segmentation techniques.Results:When comparison with manual segmentation was performed,a mean DSC value of 0.943±0.020 was achieved,outperforming other previously published techniques.A substantial reduction in processing time was also achieved using this method.Conclusions:We have developed a new segmentation technique that is both fast and accurate.This has the potential to be used to aid diagnostics and treatment planning.