NIR attribute selection for the development of vineyard water status predictive models

Abstract

Near-Infrared spectroscopy (NIR) returns full spectra in the region between 750-2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection methods for the development of vineyard water status predictive models are presented. Spectra from grapevine leaves were collected on-the-go (from a moving vehicle) along nine dates during the 2015 season in a commercial vineyard using a NIR spectrometer (1200-2100 nm). Contemporarily, the stem water potential (Ψₛₜₑₘ) was also measured in the monitored vines. A manual selection, based on Variable Importance in Projection scores (VIP scores) to choose the spectrum intervals including the most important wavelengths (interval selection), the locally most important wavelengths in the spectrum (peak selection), as well as the Interval Partial Least Squares (IPLS) were tested as attribute selection methods. The results obtained for the estimation of Ψₛₜₑₘ using the whole spectrum (R²P=0.84, RMSEP=0.167MPa) were comparable to those yielded by the three attribute selection methods: the interval selection method (R²P=0.80, RMSEP=0.186 MPa), the peak selection method (R²P=0.77, RMSEP=0.201MPa) and the IPLS (R²P ∼ 0.62-0.79, RMSEP ∼ 0.186-0.252 MPa). The highest simplification was provided by two IPLS models with three wavelengths and bandwidths of 20 and 4 nm that yielded R²P∼0.78 and RMSEP∼ 0.190 MPa. These results corroborate the suitability of a highly reduced selection of NIR wavelengths for the prediction of grapevine water status, and its utility to develop simpler multispectral devices for vineyard water status estimation.

Plant phenotyping relevance

NIRスペクトルからブドウの水分状態を推定する波長選択法を比較・検証し、簡易マルチスペクトル装置への応用可能性まで示しており、植物表現型取得法が研究の中心である。

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