In-season variable rate nitrogen topdressing recommendation and validation for winter oilseed rape cultivation supported by UAV multispectral imagery

Abstract

Efficient nitrogen (N) management is critical for maximising yield while minimising environmental impacts in oilseed rape production. While Unmanned Aerial Vehicle (UAV)-based monitoring of N status has advanced rapidly, translating estimated N status into actionable fertilization strategies remains limited. This study proposes a multistage N topdressing recommendation framework for winter oilseed rape that integrates UAV multispectral data with prior agronomic knowledge using machine learning algorithms. The framework accurately estimated the nitrogen nutrition index (NNI), with the random forest model performing best (r² =0.73 and RMSE = 0.11) for the validation dataset. By integrating estimated NNI, critical NNI thresholds, and optimal N uptake levels, dynamic, stage-specific N fertilizer topdressing rates were computed. A field experiment with varying basal N fertilizer rates was conducted to validate the framework, with UAV-guided topdressing performed after each monitoring event. Compared with the local conventional fertilization practice, the UAV-guided treatment with 90 kg N/ha basal fertilizer rates significantly improved yield by 20.2 % and N use efficiency by 80.1 %. This study bridges the gap between remote sensing-based diagnostics and in-field N fertilization, offering a feasible data-driven approach for real-time N management to enhance productivity and sustainability in oilseed rape cultivation.

Plant phenotyping relevance

UAVマルチスペクトル画像から植物の窒素栄養指数（NNI）を推定する手法を機械学習で構築・検証し、その推定に基づく施肥フレームワークを評価しており、植物状態の取得・抽出が中心的です。

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