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

Williams, Dominic; Hackett, Christine A.; Karley, Alison J.; McCallum, Susan; Smith, Kay; Britten, Avril; Graham, Julie

doi:10.48130/frures-2021-0007

Cited by 7 publications

(10 citation statements)

References 41 publications

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“…Figure 5 indicates the significance of treatment effects on a series of selected ratios and principal components. The significance effects of genotype on spectral reflectance are not presented here as these have been established in our recent study of field‐grown plants (Williams et al, 2021). Numerous significant treatment‐by‐genotype interactions were seen.…”

Section: Resultsmentioning

confidence: 99%

“…To carry out further analysis of the data, four different types of measures were used. The first was the individual wavelengths, the second was a series of wavelength ratios that had been gathered from the hyperspectral imaging literature (Yendrek et al, 2017; Zhou et al, 2021), (Table 1), and the third was a series of ratios generated from our previous research with raspberry (Williams et al, 2021). Finally, the top 10 principal components were calculated using all the data for each date (see Section 2.5) and were analyzed because they provided a simple summary of the highly correlated wavelength variables.…”

Section: Methodsmentioning

confidence: 99%

“…To date, these studies represent the few examples where hyperspectral imaging has been applied for stress detection in perennial crops. Raspberry provides a suitable model for testing the utility of spectral signatures, building on our existing knowledge of spectral signals for genotype differentiation (Williams et al, 2021), to identify genotypic responses to biotic and abiotic stress. In this study, experimental manipulation of plant stress was carried out using three contrasting plant genotypes under controlled (glasshouse) conditions to test plant responses to reduced water availability, root infection with the pathogen P. rubi , and root infestation with herbivorous vine weevil larvae.…”

Section: Introductionmentioning

confidence: 99%

“…While effort has been invested in breeding resistant varieties, the detection of suitable genetic markers for breeding could be accelerated by using high throughput methods for accurate phenotyping in field conditions. Our previous work using a raspberry mapping population has shown that hyperspectral imaging can be used to detect genotypic differences in plant physical characteristics (Williams et al, 2021). This study demonstrated that certain hyperspectral imaging wavelengths and ratios were heritable and were associated with genetic markers on linkage groups (QTLs), including some that colocated with QTLs for physical crop characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Raspberry plant stress detection using hyperspectral imaging

et al. 2023

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Monitoring plant responses to stress is an ongoing challenge for crop breeders, growers, and agronomists. The measurement of below-ground stress is particularly challenging as plants do not always show visible signs of stress in the above-ground organs, particularly at early stages. Hyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined. In this study, three genotypes of red raspberry plants grown under controlled conditions in a glasshouse were subjected to belowground biotic stresses (root pathogen Phytophthora rubi and root herbivore Otiorhynchus sulcatus) or abiotic stress (soil water availability) and regularly imaged using hyperspectral cameras over this period. Significant differences were observed in plant biophysical traits (canopy height and leaf dry mass) and canopy reflectance spectrum between the three genotypes and the imposed stress treatments. The ratio of reflectance at 469 and 523 nm showed a significant genotype-by-treatment interaction driven by differential genotypic responses to the P. rubi treatment. This indicates that spectral imaging can be used to identify variable plant stress responses in raspberry plants.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Raspberry plant stress detection using hyperspectral imaging

et al. 2023

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show abstract

“…To date, these studies represent the few examples where hyperspectral imaging has been applied for stress detection in perennial crops. Raspberry provides a suitable model for testing the utility of spectral signatures, building on our existing knowledge of spectral signals for genotype differentiation [11] , to identify genotypic responses to biotic and abiotic stress. In this study, experimental manipulation of plant stress was carried out using three contrasting plant genotypes under controlled (glasshouse) conditions to test plant responses to reduced water availability, root infection with the pathogen P. rubi and root infestation with herbivorous vine weevil larvae.…”

Section: Introductionmentioning

confidence: 99%

Raspberry plant stress detection using hyperspectral imaging

Williams

Karley

Britten

et al. 2023

Preprint

Self Cite

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Monitoring plant responses to stress is an ongoing challenge for crop breeders, growers and agronomists. The measurement of below ground stress is particularly challenging as plants do not always show visible signs of stress in the above ground organs, particularly at early stages. Hyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined. In this study, three genotypes of red raspberry plants grown under controlled conditions in a glasshouse were subjected to below ground biotic stresses (root pathogen Phytophthora rubi and root herbivore Otiorhynchus sulcatus) or abiotic stress (soil water availability) and regularly imaged using hyperspectral cameras over this period. Significant differences were observed in plant biophysical traits (canopy height and leaf dry mass) and canopy reflectance spectrum between the three genotypes and the imposed stress treatments. The ratio of reflectance at 469nm and 523nm showed a significant genotype-by-treatment interaction driven by differential genotypic responses to the Phytophthora rubi treatment. This indicates that spectral imaging can be used to identify variable plant stress responses in raspberry plants.

show abstract