Enhancing winter wheat plant nitrogen content prediction across different regions: Integration of UAV spectral data and transfer learning strategies

文献类型： 外文期刊

第一作者： Li, Zongpeng

作者： Li, Zongpeng;Cheng, Qian;Zhai, Weiguang;Mao, Bohan;Li, Yafeng;Zhou, Xinguo;Chen, Zhen;Chen, Li;Yang, Jie

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关键词： Gaussian process regression; Unequal-weight strategy; Plant nitrogen content; Transfer learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 234 卷

页码：

收录情况： SCI

摘要： Accurate prediction of plant nitrogen content (PNC) in winter wheat is crucial for precise agricultural water and fertilizer management. UAV-mounted sensors provide a non-destructive, real-time method for assessing PNC on a field scale. This study investigates the effectiveness of RGB, multispectral (MS), and hyperspectral (HS) data acquired from UAVs in predicting PNC in winter wheat, along with assessing the model's robustness across different regions. Spectral data were collected using these sensors during the flowering stage in two different regions. Spectral bands sensitive to PNC were analyzed, and spectral indices were constructed. A Gaussian process regression (GPR) algorithm is employed to integrate spectral indices from different sensors to construct yield prediction models. The performance of the prediction model is analyzed under both equal-weight and unequal-weight integration strategies. Subsequently, the prediction of PNC in winter wheat utilized the dataset from region A as the calibration set, supplemented by samples from region B. The results revealed that integrating data from all three sensors using an unequal weight strategy produced the most optimal predictive performance for both regions. Furthermore, the transfer learning model demonstrated superior performance by incorporating 18 samples from region B into the MS + HS integrated dataset from region A (R2 = 0.61, RMSE = 1.30 mg.g-1). This study confirms the potential of unequal weights integration strategy and model updating strategy based transfer learning for PNC prediction across different regions.

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