Combining Vis-NIR Spectral Data and Multivariate Technique to Estimate Nutrient Contents in Peach Leaves

Abstract

Peach tree (Prunus persica L. Batsch) is a fruit species of great economic importance worldwide. Thousands of chemical leaf analyses are performed on a yearly basis to support decision-making about fertilizer application. However, traditional methods to determine nutrient content in plant tissue require a mix of strong acids, besides being time-consuming and generating polluting waste. Visible (Vis) and near-infrared (NIR) spectroscopy combined with multivariate techniques emerges as a potential solution to overcome limitations of traditional chemical analyses. The aim of the present study is to combine Vis-NIR spectral data and multivariate techniques to test strategies for the development of models to estimate nutrient content in peach leaves. The study estimated N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn content in the leaves of peach trees grown in two locations, namely: Pelotas and Pinto Bandeira, in Southern Brazil. Therefore, local and regional scale prediction models were developed by combining preprocessed Vis-NIR spectral data to both Savitzky–Golay first-derivative (SGD1d) and partial least squares regression (PLSR) multivariate technique. Most of the proposed prediction models showed average accuracy (R2 ≥ 0.50 and <0.75, RPIQ ≥ 1.9 and <3.0). The local-1 ‘PB’ model showed higher nutrient prediction accuracy than the regional ‘PB + Pelotas’ model and the local-2 ‘Pelotas’ model. Estimates on nutrient content in peach tree leaves subjected to local, local-1 ‘PB’ and local-2 ‘Pelotas’ models fed with data collected in the same site showed better performance than calculations based on data from other sites and/or regions. Finally, the current study allowed making updates in the refinement of more sustainable techniques to set nutrient content.