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From field to airborne spectroscopy – advancing spectral data analytics for accurate retrieval of perennial ryegrass biomass and feed quality

Togeiro de Alckmin, G ORCID: 0000-0002-0665-9426 2021 , 'From field to airborne spectroscopy – advancing spectral data analytics for accurate retrieval of perennial ryegrass biomass and feed quality', Other Degree thesis, University of Tasmania.

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Abstract

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 in a weight per area basis. In addition, model prediction performance in known and new locations are compared. Data collection comprises 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 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 better or equivalent performance than previous studies which used a higher number of bands and of narrower bandwidth. Additionally, when using top of canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein in its weight per area basis (RMSEP 80 kg/ha). These models can be employed to new unseen locations results with a minor decrease in accuracy (RMSEP 85.5 kg/ha). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5 - 3.0 %DM in optimal models (requiring ten bands). However, these models display a low explanatory ability of the observed variability (R2 > 0.5), rendering it only suitable for qualitative grading.

Item Type: Thesis - Other Degree
Authors/Creators:Togeiro de Alckmin, G
Keywords: perennial ryegrass, biomass, crude protein, remote sensing, spectroscopy, drones
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Copyright 2021 the author

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