Estimating Swiss chard foliar macro- and micronutrient concentrations under different irrigation water sources using ground-based hyperspectral data and four partial least squares (PLS)-based (PLS1, PLS2, SPLS1 and SPLS2) regression algorithms

- Swiss chard nutrients were estimated under trial conditions using hyperspectral data.
- Partial least squares (PLS1 and PLSR2) and sparse PLS1 and PLS2 regressions performance was compared.
- SPLS-based regression methods significantly reduced the dimensionality in hyperspectral data.
- Macronutrients estimation models were more accurate than micronutrients estimation ones.

Timely information on crop foliar nutrient content provides a measure of crop nutritional and vitality status. Growers and farm managers use such information for precision crop management such as an appropriate fertilizer application to correct for any crop nutrient deficiencies at identified hotspots. Foliar heavy nutrient content could also be a direct indicator of crops having been polluted from the surroundings, which may be a result of heavy metals absorbed from, among others, contaminated soils and waste water. In the present study, we explored the potential use of four partial least squares (PLS)-based regression algorithms for estimating foliar Swiss chard macro- and micronutrient concentrations using ground-based hyperspectral data under three treatments; i.e. rainwater + fertilizer ('R + F'), tap water + fertilizer ('T + F'), and treated wastewater ('W'). Swiss chard canopy-level hyperspectral measurements under these three treatments were collected using a handheld spectroradiometer 2.5 months after sowing. The reflectance spectra were normalized to their first-order derivatives. The concentrations of three Swiss chard foliar macronutrients (NPK) and three micronutrients (Zn, Cu and Fe) under the three treatments were determined. Regression models for estimating macro- and micronutrient concentrations were then derived using PLS1 and sparse PLS1 methods, while the potential simultaneous estimation of the macronutrient as well as micronutrient concentrations was explored using the PLS2 and SPLS2 regression approaches. Results showed that high variances in the macro- and micronutrient concentrations can be explained by the four regression models under the three treatments (R2train ranged between 0.73 and 0.99), except when P, Zn and Cu concentrations were estimated using the PLS2-based models under the three treatments (R2train ranged between 0.08 and 0.68) and Fe concentration using SPLSR1 under 'W' treatment (R2train = 0.64). Our results further showed that Swiss chard foliar N (RMSE = 1.67%) concentration under 'R + F' treatment and Fe (RMSE = 7.83%) concentration under the 'T + F' treatment most accurately estimated macro- and micronutrients. Our study also showed that the Swiss chard foliar macronutrient concentrations were more accurately estimated compared to micronutrient concentrations and PLS2 outperformed the PLS1 based regression model. The results of the current study pave the way for developing an effective foliar nutrient estimation routine suitable for monitoring Swiss chard nutrient status under different treatments.