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Journal of Integrative Agriculture  2023, Vol. 22 Issue (8): 2536-2552    DOI: 10.1016/j.jia.2023.02.027
Agro-ecosystem & Environment Advanced Online Publication | Current Issue | Archive | Adv Search |
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
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
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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棉花NNI诊断尚缺乏研究。本研究评估支持向量机(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估测棉花全生育期叶片含量NNI的性能。研究结果表明,与氮含量NNI相关性最高的15个植被指数作为输入时,模型表现更优三种模型中XGB模型在估测含量方面表现最优。上半冠层水平下的含量估测精度(率定集R2=0.89,RMSE=0.68 g m-2RE=14.62%;验证集R2=0.83,RMSE=1.08 g m-2RE=19.71%)高于全冠层水平(率定集R2=0.73,RMSE=2.20 g m-2RE=26.70%;验证集R2=0.70,RMSE=2.48 g m-2RE=31.49%)植株水平(率定集R2=0.66,RMSE=4.46 g m-2RE=30.96%;验证集R2=0.63,RMSE=3.69 g m-2RE=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%)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。


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

Keywords:  UAV       nitrogen diagnosis       leaf nitrogen weight       nitrogen nutrition index       cotton  
Received: 02 September 2022   Accepted: 26 November 2022
Fund: This study was funded by the National Key Research and Development Program of China (2022YFD1900401) and the Chinese Universities Scientific Fund (2452020018).
About author:  PEI Sheng-zhao, E-mail:; # Correspondence FAN Jun-liang, E-mail:

Cite this article: 

PEI Sheng-zhao, ZENG Hua-liang, DAI Yu-long, BAI Wen-qiang, FAN Jun-liang. 2023. Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images. Journal of Integrative Agriculture, 22(8): 2536-2552.


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