eCite Digital Repository

Prediction of poppy thebaine alkaloid concentration using UAS remote sensing

Citation

Iqbal, F and Lucieer, A and Barry, K, Prediction of poppy thebaine alkaloid concentration using UAS remote sensing, Precision Agriculture, 21 pp. 1045-1056. ISSN 1385-2256 (2020) [Refereed Article]

Copyright Statement

© Springer Science+Business Media, LLC, part of Springer Nature 2020

DOI: doi:10.1007/s11119-020-09707-5

Abstract

Alkaloid concentration, which represents the quality of industrial poppy, needs to be estimated in a spatially explicit manner to predict the value of crop prior to harvesting. Current practice is to estimate alkaloid concentration using destructive sampling and laboratory analysis. However, in order to estimate the value of the whole crop, a method that could predict alkaloid concentration in field conditions prior to harvesting is needed. In this study, an unmanned aerial system (UAS) with multispectral imaging was tested for estimation of alkaloid concentration of a poppy crop before harvest, which was sown for pharmaceutical purposes in Tasmania, Australia. This study presents the result of a random forest (RF) regression analysis to evaluate the contribution and predictive ability of spectral and structural variables derived from the images. It was found that UAS imagery with an RF model has the potential to estimate thebaine (paramorphine) concentration well before harvesting and without laboratory analysis. It was found that an RF model with the combination of MSAVI, mSR, OSAVI, NDVI and EVI spectral indices can provide optimal results to estimate thebaine with a relative error of 13.56% to 22.36% with training and validation datasets, respectively. The thebaine concentration predicted using the proposed RF model was strongly correlated to the laboratory-measured thebaine concentration, with an R2 value ranging from 0.63 to 0.82 for the training and validation datasets, respectively. These results indicate that poppy thebaine concentration can be estimated with reasonable accuracy 3 weeks prior to harvesting.

Item Details

Item Type:Refereed Article
Keywords:poppy, alkaloids, thebaine, UAS remote sensing, random forest
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Plant Production and Plant Primary Products
Objective Group:Industrial crops
Objective Field:Plant extract crops
UTAS Author:Iqbal, F (Mr Irfan Iqbal)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
UTAS Author:Barry, K (Associate Professor Kara Barry)
ID Code:148020
Year Published:2020
Deposited By:Geography and Spatial Science
Deposited On:2021-11-30
Last Modified:2022-01-12
Downloads:0

Repository Staff Only: item control page