The Development of Hyperspectral Distribution Maps to Predict the Content and Distribution of Nitrogen and Water in Wheat (Triticum aestivum) - Bruning, Brooke; Liu, Huajian; Brien, Chris; Berger, Bettina; Lewis, Megan; Garnett, Trevor (Frontiers in Plant Science, 2019)
Quantifying plant water content and nitrogen levels and determining water and nitrogen phenotypes is important for crop management and achieving optimal yield and quality. Hyperspectral methods have the potential to advance high throughput phenotyping efforts by providing a rapid, accurate and non-destructive alternative for estimating biochemical and physiological plant traits. Our study (i) acquired hyperspectral images of wheat plants using a high throughput phenotyping system, (ii) developed regression models capable of predicting water and nitrogen levels of wheat plants and (iii) applied the regression coefficients from the best-performing models to hyperspectral images in order to develop prediction maps to visualise nitrogen and water distribution within plants. Hyperspectral images were collected of four wheat (Triticum aestivum) genotypes grown in nine soil nutrient conditions and under two water treatments. Five multivariate regression methods in combination with ten spectral pre-processing techniques were employed to find a model with strong predictive performance. Visible and near infrared wavelengths (VNIR: 400-1000nm) alone were not sufficient to accurately predict water and nitrogen content (validation R2=0.56 and R2=0.59 respectively) but model accuracy was improved when shortwave-infrared wavelengths (SWIR: 1000-2500nm) were incorporated (validation R2=0.63 and R2=0.66 respectively). Wavelength reduction produced equivalent model accuracies while reducing model size and complexity (validation R2=0.69 and R2=0.66 for water and nitrogen respectively). Developed distribution maps provided a visual representation of the concentration and distribution of water within plants whilst nitrogen maps seemed to suffer from noise. The findings and methods from this study demonstrate the high potential of high-throughput hyperspectral imagery for estimating and visualising the distribution of plant chemical properties.