Dataset Information

Predicting Zea mays Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images.

ABSTRACT: Image analysis methods for measuring crop phenotypes may replace traditional measurements if they more efficiently and reliably capture similar or superior information. This study used a recreational-grade unmanned aerial vehicle carrying a spectrally-modified consumer-grade camera to collect images in which each pixel value is a vegetation index based on the normalized difference between the blue and near infrared wavelength bands (BNDVI). The subjects of the study were Zea mays hybrids with good yield potential grown in 4-row plots. Flights were conducted at least once per week during three successive growing seasons in south-central Wisconsin. Average BNDVI for each plot (genotype) rose steadily through June, peaked in July, and then declined as plants matured. BNDVI histograms changed shape over the season as the canopy concealed soil, became more uniformly green, then senesced. Principal Components Analysis (PCA) captured the change in histogram shape. PC1 represented canopy closure. PC2 represented the mean of the BNDVI distribution. PC3 represented the spread of the distribution. Correlation analysis showed that flowering time correlated with PC2 and PC3 best (r ? 0.5) a few days before the event (day in which 50% of the plants exhibited tassels). Three ears were picked from each plot to quantify kernel dimensions by image analysis before each plot was mechanically harvested to determine grain weight per plot. Correlations between this measurement of yield and PC2 were low in June but exceeded 0.4 within 10 days after flowering. Kernel length correlated similarly with PC2. The correlation between PC2 and kernel thickness displayed a similar but inverted time course. These results indicate that greater mid-season BNDVI values correlate positively with yield comprised of tall, thin kernels. Partial least squares regression performed on the BNDVI time courses predicted flowering time (r = 0.54-0.79) and yield (r = 0.4-0.69). This three-year experiment demonstrated that readily available hardware and software can create a phenotyping platform capable of predicting maize flowering time, yield, and kernel dimensions to a useful degree.

SUBMITTER: Wu G

PROVIDER: S-EPMC6797588 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Predicting <i>Zea mays</i> Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images.

Wu Guosheng G Miller Nathan D ND de Leon Natalia N Kaeppler Shawn M SM Spalding Edgar P EP

Frontiers in plant science 20191011

Image analysis methods for measuring crop phenotypes may replace traditional measurements if they more efficiently and reliably capture similar or superior information. This study used a recreational-grade unmanned aerial vehicle carrying a spectrally-modified consumer-grade camera to collect images in which each pixel value is a vegetation index based on the normalized difference between the blue and near infrared wavelength bands (BNDVI). The subjects of the study were <i>Zea mays</i> hybrids ...[more]

PMID: 31681364

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Project description:Association mapping is a powerful approach to detect associations between traits of interest and genetic markers based on linkage disequilibrium in molecular plant breeding. The aim of this study was the identification of single nucleotide polymorphisms (SNPs) and SilicoDArT markers associated with yield traits and morphological features in maize. Plant material constituted inbred lines. The field experiment with inbred lines was established on 10 m2 plots in a set of complete random blocks in three replicates. We observed 22 quantitative traits. Association mapping was performed in this study using a method based on the mixed linear model with the population structure estimated by eigenanalysis (principal component analysis applied to all markers) and modeled by random effects. As a result of mapping, 969 markers (346 SNPs and 623 SilocoDArT) were selected from 49,911 identified polymorphic molecular markers, which were significantly associated with the analyzed morphological features and yield structure traits. Markers associated with five or six traits were selected during further analyses, including SilicoDArT 4591115 (anthocyanin coloration of anthers, length of main axis above the highest lateral branch, cob length, number of grains per cob, weight of fresh grains per cob and weight of fresh grains per cob at 15% moisture), SilicoDArT 7059939 (anthocyanin coloration of glumes of cob, time of anthesis-50% of flowering plants, time of silk emergence-50% of flowering plants, anthocyanin coloration of anthers and cob diameter), SilicoDArT 5587991 (anthocyanin coloration of glumes of cob, time of anthesis-50% of flowering plants, anthocyanin coloration of anthers, curvature of lateral branches and number of rows of grain). The two genetic similarity dendrograms between the inbred lines were constructed based on all significant SNPs and SilicoDArT markers. On both dendrograms lines clustered according to the kernel structure (flint, dent) and origin. The selected markers may be useful in predicting hybrid formulas in a heterosis culture. The present study demonstrated that molecular SNP and Silico DArT markers could be used in this species to group lines in terms of origin and lines with incomplete origin data. They can also be useful in maize in predicting the hybrid formula and can find applications in the selection of parental components for heterosis crossings.

Dataset Information

Predicting Zea mays Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images.

Publications

Predicting <i>Zea mays</i> Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images.

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