Abstract
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 nondestructive 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 visualize 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 10 spectral preprocessing techniques were employed to find a model with strong predictive performance. Visible and near infrared wavelengths (VNIR: 400–1,000nm) 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: 1,000–2,500nm) 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 while 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 visualizing the distribution of plant chemical properties.
Highlights
Wheat (Triticum aestivum) is the major winter crop in Australia and sustainable improvement of yields is a major research focus
Prior to destructive harvest of the wheat plants grown in varying nutrient and water regimes, hyperspectral images were acquired in the Visible and near infrared wavelengths (VNIR) and shortwave-infrared wavelengths (SWIR) regions
The development of distribution maps through hyperspectral imaging was demonstrated as a nondestructive, in vivo tool for estimating the concentration, and spatial distribution of water content and nitrogen levels in wheat
Summary
Wheat (Triticum aestivum) is the major winter crop in Australia and sustainable improvement of yields is a major research focus. The availability of nitrogen and water are widely recognized as two of the main factors limiting crop growth and production (Garnett and Rebetzke, 2013). Nitrogen is essential for crops but nitrogen use efficiency is generally low (Raun and Johnson, 1999). Understanding the nitrogen dynamics within the plant is key to improving fertilization practices and breeding more efficient crops (Garnett et al, 2015) which in turn may help reach the increased yields required for a growing population. Water profoundly influences plant health and potential yield. The accurate assessment of water content has importance for fertilization, irrigation practices, and drought assessment (Peñuelas et al, 1997; Torres et al, 2019)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
