Applying UAV-based crop height to monitor biomass, N-concentration, and N-uptake in winter wheat

Abstract

Abstract. Spatial knowledge for supporting precise N fertilization is of key interest in crop management. Therefore, accurate and reliable data on crop dry biomass (DB) and N concentration (Nconc), and N-uptake (Nup) are needed considering spatial heterogeneity. While N uptake in field experiments is computed using in-situ data of DB and Nconc, it also can be directly estimated with remote sensing methods. Usually, these crop traits are derived by using optical remote or proximal sensing approaches. In this contribution, we investigate a paradigm change in providing non-destructive DB, Nconc, and Nup estimates by using non-optical data analyses but structural information extraction. Numerous studies proofed UAV-derived crop height can serve as a robust estimator for biomass. Due to the well-known negative correlation between biomass and N concentration over the growing season crop height might be used as an estimator for Nconc as well. Based on these correlations we investigate three key hypotheses: (i) crop height from UAV images using a Structure from Motion and Multiview Stereopsis (SfM/MVS) workflow serves as a very robust estimator for DB, (ii) Nconc is correlated over the growing season to DB, and (iii) DB is the dominating parameter in determining Nup. Hence, the main research question of this contribution is if UAV-derived crop height (ÚAV-CH) serves as a robust estimator for DB, it also can be used to directly estimate Nconc and Nup, UAV-CH in ultra-high spatial resolution (

Plant phenotyping relevance

UAV画像のSfM/MVSワークフローで作物高を抽出し、バイオマス、窒素濃度、窒素吸収量を推定する手法が研究の中心であるため、植物フェノタイピング手法の実質的応用に該当する。

Code and data availability

The article describes UAV-based crop height phenotyping of winter wheat (2020–2022, Campus Klein-Altendorf) with SfM/MVS processing in Agisoft Metashape, but contains no data availability statement, no public repository deposit, no author code/scripts, and no supplement with datasets or models. All URLs in the text are

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