Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Title
Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data
Publication Date
2020-12
Author(s)
Suarez, Luz Angelica
( author )
OrcID: https://orcid.org/0000-0002-4233-2172
Email: lsuarezc@une.edu.au
UNE Id une-id:lsuarezc
Robson, Andrew
( author )
OrcID: https://orcid.org/0000-0001-5762-8980
Email: arobson7@une.edu.au
UNE Id une-id:arobson7
McPhee, John
O'Halloran, Julie
van Sprang, Celia
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Springer New York LLC
Place of publication
United States of America
DOI
10.1007/s11119-020-09722-6
UNE publication id
une:1959.11/51519
Abstract

Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R2 < 0.1) than similar measures from the multispectral sensors (R2 < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions.

Link
Citation
Precision Agriculture, 21(6), p. 1304-1326
ISSN
1573-1618
1385-2256
Start page
1304
End page
1326
Rights
Attribution 4.0 International

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