2021
DOI: 10.7717/peerj.12628
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3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Abstract: Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletoni… Show more

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Cited by 11 publications
(3 citation statements)
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“…Leaf angle is an important agronomic trait in maize, with a smaller leaf angle allowing higher planting density, leading to more efficient light capture and higher yields (Tross et al, 2021). Cao et al (Cao et al, 2020) cloned maize bHLH TF ZmIBH1-1 by map-based cloning, and found that it was a negative regulator of leaf angle.…”
Section: Zmbhlh112 Gene Regulates Maize Leaf Angle Developmentmentioning
confidence: 99%
“…Leaf angle is an important agronomic trait in maize, with a smaller leaf angle allowing higher planting density, leading to more efficient light capture and higher yields (Tross et al, 2021). Cao et al (Cao et al, 2020) cloned maize bHLH TF ZmIBH1-1 by map-based cloning, and found that it was a negative regulator of leaf angle.…”
Section: Zmbhlh112 Gene Regulates Maize Leaf Angle Developmentmentioning
confidence: 99%
“…(2022). A separate experiment using genotypes from the same sorghum association panel grown in a controlled greenhouse environment was described in Tross et al . (2021), and the data collected was previously described in Ge et al .…”
Section: Methodsmentioning
confidence: 99%
“…To our knowledge, ours is the first work that trains classifiers on visual sensor data to predict whether an image shows organisms with a reference or alternate version of a genetic marker in order to better understand the genotype × phenotype relationship. There is related work in genomic selection that attempts to predict end-of-season traits like leaf or grain length and crop yield (Sandhu et al, 2021 ) from genetic information, and in using 3D reconstructions of plants to identify leaf-angle related loci in the sorghum genome (Tross et al, 2021 ). In Liu et al ( 2019 ), the most related work to ours, the authors train CNNs to predict quantitative traits from SNPs, and use a visualization approach called saliency maps to highlight the SNPs that most contributed to predicting a particular trait (as opposed to predicting whether a SNP is reference or alternate, and what visual components led to that classification).…”
Section: Introductionmentioning
confidence: 99%