Ensemble Hyperspectral Band Selection for Detecting Nitrogen Status in Grape Leaves

TL;DR

This study uses ensemble feature selection on hyperspectral data to identify a minimal set of spectral bands for nitrogen detection in grape leaves, revealing non-standard bands that could enhance remote sensing accuracy.

Contribution

The paper introduces a novel ensemble feature selection pipeline that identifies a small, optimal set of spectral bands outside typical multispectral ranges for nitrogen detection in grapevines.

Findings

Less than 0.45% of bands are most informative for nitrogen status.

Selected bands are outside traditional multispectral ranges.

Potential for improved remote sensing with customized multispectral sensors.

Abstract

The large data size and dimensionality of hyperspectral data demands complex processing and data analysis. Multispectral data do not suffer the same limitations, but are normally restricted to blue, green, red, red edge, and near infrared bands. This study aimed to identify the optimal set of spectral bands for nitrogen detection in grape leaves using ensemble feature selection on hyperspectral data from over 3,000 leaves from 150 Flame Seedless table grapevines. Six machine learning base rankers were included in the ensemble: random forest, LASSO, SelectKBest, ReliefF, SVM-RFE, and chaotic crow search algorithm (CCSA). The pipeline identified less than 0.45% of the bands as most informative about grape nitrogen status. The selected violet, yellow-orange, and shortwave infrared bands lie outside of the typical blue, green, red, red edge, and near infrared bands of commercial multispectral cameras, so the potential improvement in remote sensing of nitrogen in grapevines brought forth by a customized multispectral sensor centered at the selected bands is promising and worth further investigation. The proposed pipeline may also be used for application-specific multispectral sensor design in domains other than agriculture.