Estimating Crop Nitrogen Uptake from UAV-Based Imagery Using Machine Learning Techniques

Abstract

Unmanned Aerial Vehicle (UAV)-based remote sensing using high-throughput spectral imaging has emerged as an effective non-destructive alternative for large-scale agricultural monitoring. This study evaluates the performance of UAV-based multispectral (MSI) and hyperspectral (HSI) imaging combined with machine learning for estimating in-season nitrogen uptake in spring wheat and canola. Field trials were conducted at irrigated and non-irrigated sites in southern and central Alberta, Canada, respectively, over three growing seasons (2023–2025). Coincident with ground-truth tissue sampling, aerial imagery was collected and processed to train and validate six machine learning models, using ~520 matchups per crop. All models successfully estimated nitrogen uptake across years and locations, although performance varied by sensor and data types. For canola, ANN produced the highest MSI-based accuracy (R2 = 0.83, RMSE = 0.5%), whereas HSI data improved prediction performance, with SVR achieving the best results (R2 = 0.90, RMSE = 0.40%). In wheat, ANN yielded the highest accuracy for both MSI and HSI data (R2 = 0.77, RMSE = 0.54% for MSI; R2 = 0.8, RMSE = 0.48% for HSI). These findings demonstrate that UAV-based spectral imaging combined with machine learning provides a reliable and scalable approach for non-destructive nitrogen uptake estimation. Although MSI sensors produced strong predictive performance, the enhanced spectral resolution of HSI data consistently improved estimation accuracy for both crops across varied growing conditions.

Plant phenotyping relevance

UAVマルチスペクトル・ハイパースペクトル画像と機械学習により、作物の窒素吸収量という植物形質を推定し、複数モデル・センサーの性能を評価しているため、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe UAV MSI/HSI imagery, ground-truth tissue nitrogen data, and Python/Scikit-learn modeling, but contain no data availability statement, public dataset deposit, or author code repository URL. The only external URL (Prairie Nutrient Calculator) is a generic decision-support tool used to set N 0

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