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Abstract

Pasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

Item Type:	Article
Authors/Creators:	Togeiro de Alckmin, G and Kooistra, L and Rawnsley, R and Lucieer, A
Keywords:	biomass, perennial ryegrass, vegetation index, canopy height, rising plate meter, machine learning
Journal or Publication Title:	Precision Agriculture
Publisher:	Springer New York LLC
ISSN:	1385-2256
DOI / ID Number:	https://doi.org/10.1007/s11119-020-09737-z
Copyright Information:	© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International 4.0 International (CC BY 4.0) License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Related URLs:	Google Scholar: Rawnsley, R UTAS Profile: Rawnsley, R Google Scholar: Lucieer, A UTAS Profile: Lucieer, A
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