Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313271837> ?p ?o ?g. }
- W4313271837 abstract "ABSTRACT Advancements in hyperspectral imaging (HSI) and establishment of dedicated plant phenotyping facilities have enabled researchers to gather large quantities of plant spectral images with the aim of inferring target phenotypes non-destructively. However, large volumes of data that result from HSI and corequisite specialized methods for analysis may prevent plant scientists from taking full advantage of these systems. Here, we explore estimation of physiological traits in 23 rice accessions using an automated HSI system. Under contrasting nitrogen conditions, HSI data are used to classify treatment groups with ≥ 83% accuracy by utilizing support vector machines. Out of the 14 physiological traits collected, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could also be predicted from the hyperspectral imaging data with normalized root mean square error of predictions smaller than 14% (R 2 of 0.88 for N and 0.75 for C:N). This study demonstrates the potential of using an automated HSI system to analyze genotypic variation for physiological traits in a diverse panel of rice; to help lower barriers of application of hyperspectral imaging in the greater plant science research community, analysis scripts used in this study are carefully documented and made publicly available. HIGHLIGHT Data from an automated hyperspectral imaging system are used to classify nitrogen treatment and predict leaf-level nitrogen content and carbon to nitrogen ratio during vegetative growth in rice." @default.
- W4313271837 created "2023-01-06" @default.
- W4313271837 creator A5012698965 @default.
- W4313271837 creator A5017082399 @default.
- W4313271837 creator A5039320887 @default.
- W4313271837 creator A5044206619 @default.
- W4313271837 creator A5049692788 @default.
- W4313271837 creator A5070867318 @default.
- W4313271837 creator A5076569444 @default.
- W4313271837 date "2022-12-15" @default.
- W4313271837 modified "2023-10-16" @default.
- W4313271837 title "Quantifying physiological trait variation with automated hyperspectral imaging in rice" @default.
- W4313271837 cites W1500895378 @default.
- W4313271837 cites W1553898020 @default.
- W4313271837 cites W1597224100 @default.
- W4313271837 cites W1964940342 @default.
- W4313271837 cites W1971933842 @default.
- W4313271837 cites W1974788889 @default.
- W4313271837 cites W1975304510 @default.
- W4313271837 cites W1978617972 @default.
- W4313271837 cites W1979982458 @default.
- W4313271837 cites W1991908918 @default.
- W4313271837 cites W2010887926 @default.
- W4313271837 cites W2013328968 @default.
- W4313271837 cites W2017905245 @default.
- W4313271837 cites W2031050264 @default.
- W4313271837 cites W2036417688 @default.
- W4313271837 cites W2053465517 @default.
- W4313271837 cites W2065191898 @default.
- W4313271837 cites W2084999075 @default.
- W4313271837 cites W2119605622 @default.
- W4313271837 cites W2125865984 @default.
- W4313271837 cites W2133751300 @default.
- W4313271837 cites W2137111266 @default.
- W4313271837 cites W2153299286 @default.
- W4313271837 cites W2161558103 @default.
- W4313271837 cites W2336973098 @default.
- W4313271837 cites W2498583668 @default.
- W4313271837 cites W2529792766 @default.
- W4313271837 cites W2611517298 @default.
- W4313271837 cites W2616508689 @default.
- W4313271837 cites W2617051209 @default.
- W4313271837 cites W2752728932 @default.
- W4313271837 cites W2779955274 @default.
- W4313271837 cites W2799965583 @default.
- W4313271837 cites W2896851400 @default.
- W4313271837 cites W2897347591 @default.
- W4313271837 cites W2910438511 @default.
- W4313271837 cites W2946706963 @default.
- W4313271837 cites W2949765995 @default.
- W4313271837 cites W2982589334 @default.
- W4313271837 cites W2994177045 @default.
- W4313271837 cites W3002058427 @default.
- W4313271837 cites W3005630254 @default.
- W4313271837 cites W3008623787 @default.
- W4313271837 cites W3014415973 @default.
- W4313271837 cites W3047967414 @default.
- W4313271837 cites W3084310007 @default.
- W4313271837 cites W3085020035 @default.
- W4313271837 cites W3086806830 @default.
- W4313271837 cites W3110338372 @default.
- W4313271837 cites W3121915942 @default.
- W4313271837 cites W3124397061 @default.
- W4313271837 cites W3132300737 @default.
- W4313271837 cites W3169138508 @default.
- W4313271837 cites W3201906676 @default.
- W4313271837 cites W4206112591 @default.
- W4313271837 cites W4214819320 @default.
- W4313271837 cites W4214891485 @default.
- W4313271837 cites W4254687493 @default.
- W4313271837 cites W633320881 @default.
- W4313271837 doi "https://doi.org/10.1101/2022.12.14.520506" @default.
- W4313271837 hasPublicationYear "2022" @default.
- W4313271837 type Work @default.
- W4313271837 citedByCount "0" @default.
- W4313271837 crossrefType "posted-content" @default.
- W4313271837 hasAuthorship W4313271837A5012698965 @default.
- W4313271837 hasAuthorship W4313271837A5017082399 @default.
- W4313271837 hasAuthorship W4313271837A5039320887 @default.
- W4313271837 hasAuthorship W4313271837A5044206619 @default.
- W4313271837 hasAuthorship W4313271837A5049692788 @default.
- W4313271837 hasAuthorship W4313271837A5070867318 @default.
- W4313271837 hasAuthorship W4313271837A5076569444 @default.
- W4313271837 hasBestOaLocation W43132718371 @default.
- W4313271837 hasConcept C105795698 @default.
- W4313271837 hasConcept C106934330 @default.
- W4313271837 hasConcept C12267149 @default.
- W4313271837 hasConcept C139945424 @default.
- W4313271837 hasConcept C153180895 @default.
- W4313271837 hasConcept C154945302 @default.
- W4313271837 hasConcept C159078339 @default.
- W4313271837 hasConcept C178790620 @default.
- W4313271837 hasConcept C185592680 @default.
- W4313271837 hasConcept C186060115 @default.
- W4313271837 hasConcept C199360897 @default.
- W4313271837 hasConcept C205649164 @default.
- W4313271837 hasConcept C33923547 @default.
- W4313271837 hasConcept C41008148 @default.
- W4313271837 hasConcept C537208039 @default.
- W4313271837 hasConcept C62649853 @default.