Considerations Needed for Sensing Mineral Nutrient Levels in Pasture Using a Benchtop Laser-Induced Breakdown Spectroscopy System

Abstract

Precision agriculture aims to increase yield and profits while reducing costs, waste, and environmental side-effects. This is achieved through a process of measuring, modelling and acting; for example, laser-induced breakdown spectroscopy (LIBS) can be used to measure macro and micro nutrients in crops to determine nutrient requirements. The limiting factor with quantitative LIBS analysis of plant nutrient levels is the variation between shots on the same sample. Following a review of current literature relevant to LIBS for agriculture, this work investigates whether different chemometric methods can mitigate these variations and can create quantitative calibrations for nutrient levels in fresh and dried pelletised pasture under laboratory conditions. The methods explored were Savitzky Golay filtering, multiple linear regression, principal component regression, partial least-squares regression, gaussian process regression, and artificial neural networks. The algorithms that performed best were partial least-squares with gaussian process regression (R2 of 0.93, 0.95, and 0.92 for K, Na, and Mn, respectively), principal components analysis with artificial neural networks (R2 of 0.94, 0.83, and 0.80 for Fe, Ca, and Mg, respectively), and partial least-squares with artificial neural networks (R2 of 0.77 for B). Removing the moisture from the pasture improved model R2 values by 4–5% on average. Acquiring spectra under an argon purge produced a small reduction in accuracy for some nutrients compared to models acquired in air. Including categorical data in the principal component regression and the artificial neural networks produced negligible improvements in prediction. This chapter will give an introduction of using different types of chemometric analyses on spectra generated by LIBS to measure micro and macro nutrients in pasture under laboratory conditions. It discusses the challenges faced when building models for each nutrient.