Next Article in Journal
Refining Land-Cover Maps Based on Probabilistic Re-Classification in CCA Ordination Space
Previous Article in Journal
Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning
Previous Article in Special Issue
Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery
Article

Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level

1
School of Technology, Environments and Design, University of Tasmania-Discipline of Geography and Spatial Sciences, Hobart, TAS 7005, Australia
2
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
3
Wageningen Environmental Research-Earth Informatics, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
4
Tasmanian Institute of Agriculture-Centre for Dairy, Grains and Grazing, 16-20 Mooreville Rd, Burnie, TAS 7320, Australia
5
Wageningen Livestock Research-Livestock and Environment, De Elst 1, 6700 AH Wageningen, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2958; https://doi.org/10.3390/rs12182958
Received: 27 July 2020 / Revised: 31 August 2020 / Accepted: 7 September 2020 / Published: 11 September 2020
(This article belongs to the Special Issue Advances of Remote Sensing in Pasture Management)
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.ha1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha1). 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. View Full-Text
Keywords: perennial ryegrass; hyperspectral; machine learning; crude protein; partial least squares; feature selection; variable importance perennial ryegrass; hyperspectral; machine learning; crude protein; partial least squares; feature selection; variable importance
Show Figures

Graphical abstract

MDPI and ACS Style

Togeiro de Alckmin, G.; Lucieer, A.; Roerink, G.; Rawnsley, R.; Hoving, I.; Kooistra, L. Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level. Remote Sens. 2020, 12, 2958. https://doi.org/10.3390/rs12182958

AMA Style

Togeiro de Alckmin G, Lucieer A, Roerink G, Rawnsley R, Hoving I, Kooistra L. Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level. Remote Sensing. 2020; 12(18):2958. https://doi.org/10.3390/rs12182958

Chicago/Turabian Style

Togeiro de Alckmin, Gustavo, Arko Lucieer, Gerbert Roerink, Richard Rawnsley, Idse Hoving, and Lammert Kooistra. 2020. "Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level" Remote Sensing 12, no. 18: 2958. https://doi.org/10.3390/rs12182958

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop