hyperspectral camera (micro-hyperspec imaging sensors, extended vnir version  (Headwall Photonics)

Journal: Frontiers in Plant Science

Article Title: Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress

doi: 10.3389/fpls.2022.1026472

Figure Lengend Snippet: Characterization of the function of chalky grain 5 ( OsCG5 ) in grain chalkiness using CRISPR-Cas9 knockout (KO) and Overexpression (OE) lines. (A) RT-PCR assay showing higher transcript abundance of OsCG5 in OE lines relative to WT rice (cv. Kitaake). (B) Positions of Cas9 deletions in KO#5 and KO#6 lines (1 bp and 109 bp, respectively). (C) Distribution of the ratio of grain R and G pixel intensity values in WT, KO and OE genotypes under control and heat stress (HS). The significance was estimated using two-way ANOVA. N = 7-8 plants. Scale bar=1 cm. (D) Phenotypic difference in grain chalkiness for WT, KO and OE under control and HS. Scale bar=1 cm. (E) Hyperspectral reflectance of grains from WT, KO and OE genotypes at wavelength range 650-1650 nm under control and HS. C and HS indicate control and heat stress, respectively.

Article Snippet: Briefly, grains from control and HS groups were placed on a constantly moving platform and scanned by a hyperspectral camera (Micro-Hyperspec Imaging Sensors, Extended VNIR version, Headwall Photonics, Fitchburg, MA, USA) with Exposure Time and Frame Period set to 12 ms and 18 ms, respectively.

Techniques: CRISPR, Knock-Out, Over Expression, Reverse Transcription Polymerase Chain Reaction

Journal: Sensors (Basel, Switzerland)

Article Title: Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

doi: 10.3390/s20041144

Figure Lengend Snippet: Summary of the sun-induced fluorescence (SIF) retrieval methods.

Article Snippet: Using the same UAV-based system equipped with the hyperspectral imager (Micro-Hyperspec VNIR camera, Headwall Photonics, MA, USA), Calderón et al. [ ] detected the disease infection ( Verticillium wilt (VW)) caused by the soil-borne fungus (Verticillium dahliae Kleb) on olive plants using fluorescence, temperature, and narrow-band spectral indices.

Techniques: Fluorescence, Plasmid Preparation, Sampling, Selection, Derivative Assay

Journal: Sensors (Basel, Switzerland)

Article Title: Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

doi: 10.3390/s20041144

Figure Lengend Snippet: Summary of the airborne platforms and systems used for SIF estimations until 2019.

Article Snippet: Using the same UAV-based system equipped with the hyperspectral imager (Micro-Hyperspec VNIR camera, Headwall Photonics, MA, USA), Calderón et al. [ ] detected the disease infection ( Verticillium wilt (VW)) caused by the soil-borne fungus (Verticillium dahliae Kleb) on olive plants using fluorescence, temperature, and narrow-band spectral indices.

Techniques: Fluorescence, Imaging

Journal: Sensors (Basel, Switzerland)

Article Title: Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

doi: 10.3390/s20041144

Figure Lengend Snippet: Summary of the UAV platforms and systems used for SIF-related studies until 2019.

Article Snippet: Using the same UAV-based system equipped with the hyperspectral imager (Micro-Hyperspec VNIR camera, Headwall Photonics, MA, USA), Calderón et al. [ ] detected the disease infection ( Verticillium wilt (VW)) caused by the soil-borne fungus (Verticillium dahliae Kleb) on olive plants using fluorescence, temperature, and narrow-band spectral indices.

Techniques: Imaging, Spectroscopy

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: Classification of hyperspectral phenotyping time-points as vegetative (VEG), heading (HEAD), and grain fill (GF) phenological stages

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: A graphical representation of the unbalanced nature of the hyperspectral reflectance phenotypic data. Four site-years are represented: 2014-15 Optimal Flat, 2014-15 Severe Drought, 2016-17 Optimal Flat, and 2016-17 Severe Drought. The histograms represent heading dates. Each dashed line corresponds to a hyperspectral phenotyping date colored according to the predominant growth stage of the lines at the time of phenotyping.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: Broad-sense heritabilities of the hyperspectral wavelengths for each phenotyping time-point within each site-year. Each boxplot represents the distribution of broad-sense heritability values for the 62 hyperspectral wavelengths observed. The colors correspond to the developmental growth stage of the site-year at the time of hyperspectral phenotyping.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: Empirical correlations between grain yield and hyperspectral reflectance BLUEs within each site-year. Each solid line represents a phenotyping time-point and the Pearson’s correlation between grain yield and the 62 hyperspectral wavelengths observed. Lines are colored according to the predominant developmental growth stage of the site-year at the time of hyperspectral phenotyping. Correlation values ≥ |0.10| are significant at a level of 0.001, as denoted by the dotted red lines.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: Within site-year prediction accuracies, with and without correction for DTHD. Accuracy is expressed as the average Pearson’s correlation between predictions and observed BLUPs for GY across 20 random TRN-TST partitions. Results shown according to the type of relationship matrix tested: Genomic (G), pedigree (A), individual hyperspectral time-points ( e.g. , H.10Jan, H.23Mar, etc.), hyperspectral BLUEs for each developmental growth stage (H.VEG, H.HEAD, H.GF), and hyperspectral BLUEs across all time-points (H.ALL). The color corresponds to the predominant developmental growth stage of the site-year at the time of phenotyping. Error bars are the standard deviation of prediction accuracy for the 20 random partitions.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques: Standard Deviation

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: The relationship between individual hyperspectral time-point prediction accuracy and the average magnitude of the correlations between hyperspectral bands and GY. The absolute values of the correlations between hyperspectral bands and GY were calculated and then averaged across the 62 bands for each time-point within each site-year. Plotted on the x-axis, this represents the average strength of the relationship between hyperspectral reflectance and GY for each time-point within each site-year. The y-axis shows the prediction accuracy for each individual hyperspectral time-point in within site-year prediction.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques:

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: Within breeding cycle/across managed treatments prediction accuracies, with and without correction for DTHD. Accuracy is expressed as the average Pearson’s correlation between predictions and observed BLUPs for GY across 20 random TRN-TST partitions. In each partition, the TRN set consisted of all records from four out of the five managed treatments within the breeding cycle plus 20% of records from the fifth managed treatment. Single-kernel models tested were genetic main effects only (G or A) and hyperspectral reflectance main effects (H.VEG, H.HEAD, H.GF, H.ALL). Multi-kernel models assessed were genetic main effects plus genetic GxE (G + G GxE , A + A GxE ) and genetic main effects plus hyperspectral reflectance GxE ( e.g. , G + H.VEG GxE , A + H.ALL GxE , etc.). The color corresponds to the developmental growth stage of the site-year at the time of phenotyping. Error bars are the standard deviation of prediction accuracy for the 20 random partitions.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques: Standard Deviation

Journal: G3: Genes|Genomes|Genetics

Article Title: Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

doi: 10.1534/g3.118.200856

Figure Lengend Snippet: Across breeding cycles/within managed treatment prediction accuracies, with and without correction for DTHD. Accuracy is expressed as the average Pearson’s correlation between predictions and observed BLUPs for GY across 20 random TRN-TST partitions. In each partition, the TRN set consisted of all records from three out of the four of the breeding cycles for the managed treatment plus 20% of records from the fourth breeding cycle. Single-kernel models tested were genetic main effects only (G or A) and hyperspectral reflectance main effects (H.VEG, H.HEAD, H.GF, H.ALL). Multi-kernel models assessed were genetic main effects plus genetic GxE (G + G GxE , A + A GxE ) and genetic main effects plus hyperspectral reflectance GxE ( e.g. , G + H.VEG GxE , A + H.ALL GxE , etc.). The color corresponds to the developmental growth stage of the site-year at the time of phenotyping. Error bars are the standard deviation of prediction accuracy for the 20 random partitions.

Article Snippet: Hyperspectral reflectance data were collected with a hyperspectral camera (A-series, Micro-Hyperspec VNIR, Headwall Photonics, Fitchburg, MA, USA) as part of the Alava Remote Sensing Spectral Solution (ARS3, Alava Ingenieros, Madrid, Spain) mounted in a Piper PA-16 Clipper aircraft.

Techniques: Standard Deviation