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Winter Wheat Nitrogen Content Prediction and Transferability of Models Based on UAV Image Features

文献类型： 外文期刊

作者： Zhang, Jing 1 ; Cheng, Gong 2 ; Huang, Shaohui 1 ; Yang, Junfang 1 ; Yang, Yunma 1 ; Xing, Suli 1 ; Wang, Jingxia 1 ; Yang, Huimin 1 ; Nie, Haoliang 1 ; Yang, Wenfang 1 ; Yu, Kang 3 ; Jia, Liangliang 1 ;

作者机构： 1.Hebei Acad Agr & Forestry Sci, Inst Agr Resources & Environm, Hebei Key Lab Soil Fertil Improvement & Agr Green, 598 West Heping Rd, Shijiazhuang 050051, Peoples R China

2.Chinese Acad Sci, Inst Genet & Dev Biol, Ctr Agr Resources Res, Key Lab Agr Water Resources,Hebei Lab Water Saving, Shijiazhuang 050021, Peoples R China

3.Tech Univ Munich, Sch Life Sci, Precis Agr Lab, Durnast 9, D-85354 Freising Weihenstephan, Germany

关键词： precision nitrogen management; multispectral remote sensing; agricultural sustainability; feature fusion; machine learning regression

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 13 期

页码：

收录情况： SCI

摘要： Accurate and timely monitoring of plant nitrogen content (PNC) is essential for precision agriculture (PA) and food security. While multispectral unmanned aerial vehicle (UAV) imagery has shown promise in PNC estimation, the optimal feature combination methods of spectral and texture features remain underexplored, and model transferability across different agricultural practices is poorly understood. This study aims to present an innovative approach by integrating 40 texture features and 22 spectral features from UAV multispectral images with machine learning (ML) methods (RF, SVR, and XGBoost) for winter wheat nitrogen content prediction. In addition, through analysis of an 8-year long-term field experiment with rigorous data, the results indicated that (1) the RF and XGboost models incorporating both spectral and texture features achieved good prediction accuracy, with R2 values of 0.98 and 0.99, respectively, RMSE values of 0.10 and 0.07, and MAE values of 0.07and 0.05; (2) models trained on Farmers' Practice (FP) data showed superior transferability to Ecological Intensification (EI) conditions (R2 = 0.98, RMSE = 0.08, and MAE = 0.05 for XGBoost), while EI-trained models performed less well when applied to FP conditions (R2 = 0.89, RMSE = 0.45, and MAE = 0.35 for XGBoost). These findings established an effective framework for UAV-based PNC monitoring, demonstrating that fused spectral-textural features with FP-trained XGboost can achieve both high accuracy and practical transferability, offering valuable decision-support tools for precision nitrogen management in different farming systems.

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