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Comparing methods to estimate perennial ryegrass biomass: Canopy height and spectral vegetation indices

Citation

Togeiro de Alckmin, G and Kooistra, L and Rawnsley, R and Lucieer, A, Comparing methods to estimate perennial ryegrass biomass: Canopy height and spectral vegetation indices, Precision Agriculture, 22 pp. 205-225. ISSN 1385-2256 (2021) [Refereed Article]


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© 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.

DOI: doi:10.1007/s11119-020-09737-z

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 Details

Item Type:Refereed Article
Keywords:biomass, perennial ryegrass, vegetation index, canopy height, rising plate meter, machine learning
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Animal Production and Animal Primary Products
Objective Group:Pasture, browse and fodder crops
Objective Field:Sown pastures (excl. lucerne)
UTAS Author:Togeiro de Alckmin, G (Mr Gustavo Togeiro de Alckmin)
UTAS Author:Rawnsley, R (Dr Richard Rawnsley)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:148011
Year Published:2021
Web of Science® Times Cited:13
Deposited By:Geography and Spatial Science
Deposited On:2021-11-30
Last Modified:2021-12-23
Downloads:5 View Download Statistics

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