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

Jing Zhang, Gong Cheng (), Shaohui Huang, Junfang Yang, Yunma Yang, Suli Xing, Jingxia Wang, Huimin Yang, Haoliang Nie, Wenfang Yang, Kang Yu and Liangliang Jia ()
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Jing Zhang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Gong Cheng: Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Water-Saving Agriculture, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, The Chinese Academy of Sciences, Shijiazhuang 050021, China
Shaohui Huang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Junfang Yang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Yunma Yang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Suli Xing: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Jingxia Wang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Huimin Yang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Haoliang Nie: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Wenfang Yang: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China
Kang Yu: Precision Agriculture Laboratory, School of Life Sciences, Technical University of Munich, Dürnast 9, 85354 Freising, Germany
Liangliang Jia: Hebei Key Laboratory of Soil Fertility Improvement and Agricultural Green Development, Institute of Agricultural Resources and Environment, Hebei Academy of Agriculture and Forestry Sciences, No. 598 West Heping Road, Shijiazhuang 050051, China

Agriculture, 2025, vol. 15, issue 13, 1-18

Abstract: 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 R 2 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 (R 2 = 0.98, RMSE = 0.08, and MAE = 0.05 for XGBoost), while EI-trained models performed less well when applied to FP conditions (R 2 = 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.

Keywords: precision nitrogen management; multispectral remote sensing; agricultural sustainability; feature fusion; machine learning regression (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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