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.
Highlights
Genotype-to-phenotype studies will produce stronger conclusions and the process of improving quantitative traits will accelerate if the methods for measuring crop plant phenotypes are as discriminating as the next-generation DNA sequencing methods used to characterize the genotypes (Edwards et al, 2013; Bevan et al, 2017)
Growing in popularity is the small unmanned aerial vehicle (UAV) programmed to fly along a path defined by a series of waypoints marked by global positioning system (GPS) coordinates (Colomina and Molina, 2014; Yang et al, 2017)
A dip in BNDVI was consistently observed a few days preceding flowering for all genotypes in 2016
Summary
Genotype-to-phenotype studies will produce stronger conclusions and the process of improving quantitative traits will accelerate if the methods for measuring crop plant phenotypes are as discriminating as the next-generation DNA sequencing methods used to characterize the genotypes (Edwards et al, 2013; Bevan et al, 2017). Image analysis methods have the potential to provide the needed phenotype data. When successfully applied, they measure standard traits more precisely, objectively, and automatically than manual methods, and they can measure informative features for which there is no manual equivalent. Cameras and other sensors mounted to ground-based vehicles, overhead gantries, or cable supports can collect detailed phenotype information (Furbank and Tester, 2011; Montes et al, 2011; White et al, 2012; Deery et al, 2014). Numerous studies used UAV platforms to measure maize plant lodging (Chu et al, 2017), ground canopy cover in wheat (Yu et al, 2017), plant density, early vigor, and radiation interception in maize (Liebisch et al, 2015), as well as soil and plant interactions, and weed management (Shi et al, 2016)
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