High-throughput Phenotyping of Maize Roots Using Digital Image Analysis
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Fecha
2024Autor
Coronado Aleans, Verónica
Barrera Sánchez, Carlos F.
Guzmán, Manuel
Publicador
Corporación colombiana de investigación agropecuaria - AGROSAVIAPalabras clave
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Resumen
Recent research on maize root architecture has made significant progress, but further research is
needed to optimize methods for efficient and accurate acquisition of root architecture data. This study aimed
to assess the effectiveness of digital imaging for root phenotyping of Zea mays L. Field experiments were carried
out at two locations in the province of Antioquia, Colombia, in 2019 and 2020 to analyze root architecture
variables of 12 genotypes of maize. Two methodologies were used: manual phenotyping and digital image
analysis. Pearson’s correlation coefficients among variables were estimated. Principal Component Analysis
(PCA) was used to summarize and uncover clustering patterns in the multivariate data set. The results indicated
correlations between diameter (r = 0.94) and manually measured root diameter. The manually measured right
and left root angles correlated with image-derived root angle at r = 0.92 and 0.88, respectively, and root length
at r = 0.62. The PCA highlighted that the digital method explained the highest proportion of variation in root
areas and diameters, while the manual method dominated in root angle variables. These results corroborate a
feasible method to optimize root architecture phenotyping for research questions. This protocol can be
adopted under the automatic analysis with REST software for acquiring images of variables associated with
roots’ angle, length, and diameter.
Parte del recurso
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Ciencia y Tecnología Agropecuaria; Vol 25, Núm.1 (2023): Ciencia y Tecnología Agropecuaria; p. 1-16.
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