Journal of Integrative Agriculture ›› 2023, Vol. 22 ›› Issue (8): 2536-2552.DOI: 10.1016/j.jia.2023.02.027

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基于无人机多光谱影像的膜下滴灌棉花氮素营养诊断

  

  • 收稿日期:2022-09-02 接受日期:2022-11-26 出版日期:2023-08-20 发布日期:2022-11-26

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

PEI Sheng-zhao, ZENG Hua-liang, DAI Yu-long, BAI Wen-qiang, FAN Jun-liang   

  1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, P.R.China
  • Received:2022-09-02 Accepted:2022-11-26 Online:2023-08-20 Published:2022-11-26
  • About author:PEI Sheng-zhao, E-mail: pszvic97@163.com; # Correspondence FAN Jun-liang, E-mail: nwwfjl@163.com
  • Supported by:
    This study was funded by the National Key Research and Development Program of China (2022YFD1900401) and the Chinese Universities Scientific Fund (2452020018).

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摘要：

遥感技术已经越来越多地用于监测大面积植株的氮素状况和精准氮素养分管理。氮营养指数（nitrogen nutrition index，NNI）可以定量描述作物的氮素状况。然而，基于无人机多光谱影像的棉花NNI诊断尚缺乏研究。本研究评估了支持向量机（support vector machine，SVM）、反向传播神经网络（back propagation neural network，BPNN）和极端梯度提升（extreme gradient boosting，XGB）三种机器学习模型基于无人机多光谱影像估测棉花全生育期叶片氮含量和NNI的性能。研究结果表明，与氮含量和NNI相关性最高的前15个植被指数作为输入时，模型表现更优。三种模型中XGB模型在估测氮含量方面表现最优。上半冠层水平下的氮含量估测精度（率定集R2=0.89，RMSE=0.68 g m-2，RE=14.62%；验证集R2=0.83，RMSE=1.08 g m-2，RE=19.71%）远高于全冠层水平（率定集R2=0.73，RMSE=2.20 g m-2，RE=26.70%；验证集R2=0.70，RMSE=2.48 g m-2，RE=31.49%）和植株水平（率定集R2=0.66，RMSE=4.46 g m-2，RE=30.96%；验证集R2=0.63，RMSE=3.69 g m-2，RE=24.81%）。与之类似， XGB模型（率定集R2=0.65，RMSE=0.09，RE=8.59%；验证集R2=0.63，RMSE=0.09，RE=8.87%）在估测NNI方面也优于SVM 模型（率定集R2=0.62，RMSE=0.10，RE=7.92%； 验证集R2=0.60，RMSE=0.09，RE=8.03%）和BPNN模型（率定集R2=0.64，RMSE=0.09，RE=9.24%；验证集R2=0.62，RMSE=0.09，RE=8.38%）。基于最优XGB模型生成的NNI预测图，可以直观地诊断棉田氮素营养的空间分布和动态过程。本研究可以帮助农户及时、准确地实施棉花氮素精准管理。

Abstract:

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R2=0.89, RMSE=0.68 g m–2, RE=14.62% for calibration and R2=0.83, RMSE=1.08 g m–2, RE=19.71% for validation) was much better than that at the all-leaf level (R2=0.73, RMSE=2.20 g m–2, RE=26.70% for calibration and R2=0.70, RMSE=2.48 g m–2, RE=31.49% for validation) and at the plant level (R2=0.66, RMSE=4.46 g m–2, RE=30.96% for calibration and R2=0.63, RMSE=3.69 g m–2, RE=24.81% for validation). Similarly, the XGB model (R2=0.65, RMSE=0.09, RE=8.59% for calibration and R2=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R2=0.62, RMSE=0.10, RE=7.92% for calibration and R2=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R2=0.64, RMSE=0.09, RE=9.24% for calibration and R2=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner

Key words: UAV , nitrogen diagnosis , leaf nitrogen weight , nitrogen nutrition index , cotton