Estimation of Nitrogen Content in Alfalfa Plants Based on Multi-Source Feature Fusion.

Abstract

Plant nitrogen content (PNC) is a core physiological parameter characterizing crop nitrogen nutrition status. Its precise and dynamic monitoring is crucial for crop growth diagnosis, optimizing nitrogen fertilizer management, enhancing fertilizer use efficiency, and reducing agricultural nonpoint source pollution. This study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa. By combining TFVs to construct texture indices (TIs), variables exhibiting extremely significant correlations with alfalfa PNC ( p R 2 increased by 5.4-19.7%, 1.7-16.4%, and 5.2-17.2% for the branching, budding, and initial flowering stages, respectively. (3) The XG-Boost model demonstrated optimal performance across all growth stages and input variables. Particularly during the budding stage, the VIs + TIs model achieved the highest fitting accuracy: training set R 2 = 0.81, RMSE = 0.15%; validation set R 2 = 0.80, RMSE = 0.12%. In summary, integrating multispectral vegetation indices and texture indices effectively enhances the accuracy of PNC estimation in alfalfa, providing scientific support for precision field management and fertilization decisions in alfalfa cultivation.

Plant phenotyping relevance

UAVマルチスペクトル画像から植​​物窒素含有量を推定する特徴抽出・モデル構築と検証が研究の中心であり、植物の生理形質を測定するフェノタイピング手法に該当する。

Code and data availability

The article describes UAV multispectral imagery, PNC measurements, and machine learning models for alfalfa nitrogen estimation, but provides no public dataset, image, code, or model deposit. The Data Availability Statement states all data are incorporated into the article, and no author URLs or repository identifiers (

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