Retrieval of crude protein in perennial ryegrass using spectral data at the Canopy level

Gustavo Togeiro de Alckmin*, Arko Lucieer, Gerbert Roerink, Richard Rawnsley, Idse Hoving, Lammert Kooistra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model's prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350-2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400-1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha-1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha-1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5-3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading.

Original languageEnglish
Article number2958
JournalRemote Sensing
Volume12
Issue number18
DOIs
Publication statusPublished - 11 Sep 2020

Keywords

  • Crude protein
  • Feature selection
  • Hyperspectral
  • Machine learning
  • Partial least squares
  • Perennial ryegrass
  • Variable importance

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