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UAV-borne hyperspectral estimation of nitrogen content in tobacco leaves based on ensemble learning methods

文献类型： 外文期刊

作者： Zhang, Mingzheng 1 ; Chen, Tian'en 2 ; Gu, Xiaohe 2 ; Kuai, Yan 4 ; Wang, Cong 2 ; Chen, Dong 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang, Jiangsu, Peoples R China

2.Nongxin Smart Agr Res Inst, Nanjing, Jiangsu, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

4.Yunnan Tobacco Co, Tobacco Co Dali Prefecture, Dali, Yunnan, Peoples R China

5.Room 818, Nongke Bldg, Beijing 1000080, Peoples R China

关键词： Hyperspectral remote sensing; Unmanned aerial vehicle; Leaf nitrogen content; Heterogeneous performance; Ensemble learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 211 卷

页码：

收录情况： SCI

摘要： Fast, accurate, and real-time detection of nitrogen content in tobacco leaves is of great significance for monitoring the quality of tobacco leaves. Hyperspectral remote sensing (HRS) coupled with unmanned aerial vehicle (UAV) platform can provide unprecedented spectral information of field plants on a large scale. And with the support of various machine learning algorithms, a series of efficient models for leaf nitrogen content (LNC) assessment can be developed. This study aimed to develop a high-performance model to estimate the LNC of tobacco using UAV-borne HRS image data. Meanwhile, to cope with the heterogeneous performance problem of the individual model, ensemble learning strategies were applied to assemble multiple estimators, including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), adaptive boosting (Adaboost), and stacking to mine more valid data features. To accurately assess the performance of the established models, the coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were introduced as the evaluation indicators, and partial least squares regression (PLSR) was selected as the baseline model. Results on the test set showed that all ensemble learning methods outperformed PLSR (R2=0.680, RMSE=5.402 mg/g, 19.72%). Specifically, the stacking-based models achieved the highest accuracy as well as relatively high stability (R2=0.745, RMSE=4.825 mg/g, 17.98%). This study provides a reference for efficient and non-destructive detection of LNC or other vegetation phenotypic traits using UAV-borne HRS technology.

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