[1] |
Foster TM, Bassil NV, Dossett M, Worthington ML, Graham J. 2019. Genetic and genomic resources for Rubus breeding: A roadmap for the future. Horticulture research 6:1−9 doi: 10.1038/s41438-018-0066-6
|
[2] |
Bailey-Serres J, Parker JE, Ainsworth EA, Oldroyd GED, Schroeder JI. 2019. Genetic strategies for improving crop yields. Nature 575:109−18 doi: 10.1038/s41586-019-1679-0
|
[3] |
Walter A, Liebisch F, Hund A. 2015. Plant phenotyping: from bean weighing to image analysis. Plant Methods 11:14 doi: 10.1186/s13007-015-0056-8
|
[4] |
Ghanem ME, Marrou H, Sinclair TR. 2015. Physiological phenotyping of plants for crop improvement. Trends in Plant Science 20:139−44 doi: 10.1016/j.tplants.2014.11.006
|
[5] |
Pauli D, Andrade-Sanchez P, Carmo-Silva AE, Gazave E, French AN, et al. 2016. Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton. G3 Genes|Genomes|Genetics 6:865−79 doi: 10.1534/g3.115.023515
|
[6] |
Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. 2018. Translating high-throughput phenotyping into genetic gain. Trends in Plant Science 23:451−66 doi: 10.1016/j.tplants.2018.02.001
|
[7] |
Leucker M, Wahabzada M, Kersting K, Peter M, Beyer W, et al. 2016. Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance. Functional Plant Biology 44:1−9 doi: 10.1071/FP16121
|
[8] |
Banerjee BP, Joshi S, Thoday-Kennedy E, Pasam RK, Tibbits J, et al. 2020. High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response. Journal of Experimental Botany 71:4604−15 doi: 10.1093/jxb/eraa143
|
[9] |
Herzig P, Backhaus A, Seiffert U, Von Wirén N, Pillen K, et al. 2019. Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population. Plant Science 285:151−64 doi: 10.1016/j.plantsci.2019.05.008
|
[10] |
Coupel-Ledru A, Pallas B, Delalande M, Boudon F, Carrié E, et al. 2019. Multi-scale high-throughput phenotyping of apple architectural and functional traits in orchard reveals genotypic variability under contrasted watering regimes. Horticulture Research 6:52 doi: 10.1038/s41438-019-0137-3
|
[11] |
Pauli D, Chapman SC, Bart R, Topp CN, Lawrence-Dill CJ, et al. 2016. The quest for understanding phenotypic variation via integrated approaches in the field environment. Plant Physiology 172:622−34 doi: 10.1104/pp.16.00592
|
[12] |
Williams D, Aitkenhead M, Karley AJ, Graham J, Jones HG. 2018. Use of Imaging Technologies for High Throughput Phenotyping. In Raspberry, eds. Graham J, Brennan R. Switzerland: Springer, Cham. pp. 145−58 https://doi.org/10.1007/978-3-319-99031-6_9
|
[13] |
Liang Z, Pandey P, Stoerger V, Xu Y, Qiu Y, et al. 2017. Conventional and hyperspectral time-series imaging of maize lines widely used in field trials. GigaScience 7:gix117 doi: 10.1093/gigascience/gix117
|
[14] |
Moghimi A, Yang C, Miller ME, Kianian SF, Marchetto PM. 2018. A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging. Frontiers in Plant Science 9:1182 doi: 10.3389/fpls.2018.01182
|
[15] |
Gutiérrez S, Fernández-Novales J, Diago MP, Tardaguila J. 2018. On-The-Go hyperspectral imaging under field conditions and machine learning for the classification of grapevine varieties. Frontiers in Plant Science 9:1102 doi: 10.3389/fpls.2018.01102
|
[16] |
Jones HG. 2013. Plants and microclimate: a quantitative approach to environmental plant physiology. UK: Cambridge University Press https://doi.org/10.1017/CBO9780511845727
|
[17] |
Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP. 2019. On-the-go hyperspectral imaging for the in-field estimation of grape berry soluble solids and anthocyanin concentration. Australian Journal of Grape and Wine Research 25:127−33 doi: 10.1111/ajgw.12376
|
[18] |
Feng H, Guo Z, Yang W, Huang C, Chen G, et al. 2017. An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice. Scientific Reports 7:4401 doi: 10.1038/s41598-017-04668-8
|
[19] |
Sun D, Cen H, Weng H, Wan L, Abdalla A, et al. 2019. Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality. Plant Methods 15:54 doi: 10.1186/s13007-019-0432-x
|
[20] |
Barnaby JY, Huggins TD, Lee H, McClung AM, Pinson SRM, et al. 2020. Vis/NIR hyperspectral imaging distinguishes sub-population, production environment, and physicochemical grain properties in rice. Scientific reports 10:9284 doi: 10.1038/s41598-020-65999-7
|
[21] |
Williams D, Britten A, McCallum S, Jones H, Aitkenhead M, et al. 2017. A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions. Plant Methods 13:74 doi: 10.1186/s13007-017-0226-y
|
[22] |
Kassim A, Poette J, Paterson A, Zait D, McCallum S, et al. 2009. Environmental and seasonal influences on red raspberry anthocyanin antioxidant contents and identification of quantitative traits loci (QTL). Molecular Nutrition & Food Research 53:625−34 doi: 10.1002/mnfr.200800174
|
[23] |
McCallum S, Woodhead M, Hackett CA, Kassim A, Paterson A, et al. 2010. Genetic and environmental effects influencing fruit colour and QTL analysis in raspberry. Theoretical and Applied Genetics 121:611−27 doi: 10.1007/s00122-010-1334-5
|
[24] |
Simpson CG, Cullen DW, Hackett CA, Smith K, Hallett PD, et al. 2017. Mapping and expression of genes associated with raspberry fruit ripening and softening. Theoretical and Applied Genetics 130:557−72 doi: 10.1007/s00122-016-2835-7
|
[25] |
Graham J, Smith K, McCallum S, Hedley PE, Cullen DW, et al. 2015. Towards an understanding of the control of 'crumbly' fruit in red raspberry. SpringerPlus 4:223 doi: 10.1186/s40064-015-1010-y
|
[26] |
Graham J, Hackett CA, Smith K, Woodhead M, MacKenzie K, et al. 2011. Towards an understanding of the nature of resistance to Phytophthora root rot in red raspberry. Theoretical and applied genetics 123:585−601 doi: 10.1007/s00122-011-1609-5
|
[27] |
Graham J, Hackett CA, Smith K, Karley AJ, Mitchell C, et al. 2014. Genetic and environmental regulation of plant architectural traits and opportunities for pest control in raspberry. Annals of Applied Biology 165:318−28 doi: 10.1111/aab.12134
|
[28] |
Graham J, Hackett CA, Smith K, Woodhead M, Hein I, et al. 2009. Mapping QTLs for developmental traits in raspberry from bud break to ripe fruit. Theoretical and applied genetics 118:1143−55 doi: 10.1007/s00122-009-0969-6
|
[29] |
Hackett CA, Milne L, Smith K, Hedley P, Morris J, et al. 2018. Enhancement of Glen Moy × Latham raspberry linkage map using GbS to further understand control of developmental processes leading to fruit ripening. BMC Genetics 19:59 doi: 10.1186/s12863-018-0666-z
|
[30] |
Woodhead M, Williamson S, Smith K, McCallum S, Jennings N, et al. 2013. Identification of quantitative trait loci for cane splitting in red raspberry (Rubus idaeus). Molecular Breeding 31:111−22 doi: 10.1007/s11032-012-9775-y
|
[31] |
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, et al. 2020. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Molecular Plant 13:187−214 https://doi.org/10.1016/j.molp.2020.01.008
|
[32] |
Graham J, Smith K, MacKenzie K, Hackett C, Powell W. 2004. The construction of a genetic linkage map of red raspberry (Rubus idaeus subsp. idaeus) based on AFLPs, genomic-SSR and EST-SSR markers. Theoretical and Applied Genetics 109:740–49 https://doi.org/10.1007/s00122-004-1687-8
|
[33] |
Grattapaglia D, Sederoff R. 1994. Genetic linkage maps of Eucalyptus grandis and Eucalyptus urophylla using a pseudo-testcross: mapping strategy and RAPD markers. Genetics 137:1121−37 doi: 10.1093/genetics/137.4.1121
|
[34] |
Graham J, Smith K, Tierney I, MacKenzie K, Hackett CA. 2006. Mapping gene H controlling cane pubescence in raspberry and its association with resistance to cane botrytis and spur blight, rust and cane spot. Theoretical and Applied Genetics 112:818−31 doi: 10.1007/s00122-005-0184-z
|
[35] |
Graham J, Jennings N. 2009. Raspberry breeding. In Breeding Plantation Tree Crops: Temperate Species, eds. Priyadarshan PM, Jain SM. NY: Springer New York. pp. 233−48 https://doi.org/10.1007/978-0-387-71203-1_7
|
[36] |
Woodhead M, Weir A, Smith K, McCallum S, MacKenzie K, et al. 2010. Functional Markers for Red Raspberry. Journal of the American Society for Horticultural Science 135:418−27 doi: 10.21273/JASHS.135.5.418
|
[37] |
Dobson P, Graham J, Stewart D, Brennan R, Hackett CA, et al. 2012. Over-seasons analysis of quantitative trait loci affecting phenolic content and antioxidant capacity in raspberry. Journal of Agricultural and Food Chemistry 60:5360−6 doi: 10.1021/jf3005178
|
[38] |
Paterson A, Kassim A, McCallum S, Woodhead M, Smith K, et al. 2013. Environmental and seasonal influences on red raspberry flavour volatiles and identification of quantitative trait loci (QTL) and candidate genes. Theoretical and Applied Genetics 126:33−48 doi: 10.1007/s00122-012-1957-9
|
[39] |
MacKenzie K, Williamson S, Smith K, Woodhead M, McCallum S, et al. 2015. Characterisation of Gene H in red raspberry: explaining its role in cane morphology, disease resistance and timing of fruit ripening. Journal of Horticulture 2:144 doi: 10.4172/2376-0354.1000144
|
[40] |
Lichtenthaler HK, Wellburn AR. 1983. Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Biochemical Society Transactions 11:591−92 doi: 10.1042/bst0110591
|
[41] |
Churchill GA, Doerge RW. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963−71 doi: 10.1093/genetics/138.3.963
|