Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV

文献类型： 外文期刊

第一作者： Xu, Sizhe

作者： Xu, Sizhe;Xu, Xingang;Meng, Yang;Yang, Guijun;Feng, Haikuan;Yang, Min;Zhu, Qilei;Xue, Hanyu;Wang, Binbin;Xu, Sizhe;Xu, Xingang;Zhu, Qingzhen;Xue, Hanyu;Wang, Binbin

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关键词： UAV remote sensing; Leaf nitrogen content; Image fusion; Multiple features combination; Optimal feature variable; Machine learning; Rice

期刊名称：PRECISION AGRICULTURE （ 影响因子：6.2； 五年影响因子：6.2 ）

ISSN： 1385-2256

年卷期： 2023 年

页码：

收录情况： SCI

摘要： Timely and accurately monitoring leaf nitrogen content (LNC) is essential for evaluating crop nutrition status. Currently, Unmanned Aerial Vehicles (UAV) imagery is becoming a potentially powerful tool of assessing crop nitrogen status in fields, but most of crop nitrogen estimates based on UAV remote sensing usually use single type imagery, the fusion information from different types of imagery has rarely been considered. In this study, the fusion images were firstly made from the simultaneously acquired digital RGB and multi-spectral images from UAV at three growth stages of rice, and then couple the selecting methods of optimal features with machine learning algorithms for the fusion images to estimate LNC in rice. Results showed that the combination with different types of features could improve the models' accuracy effectively, the combined inputs with bands, vegetation indices (VIs) and Grey Level Co-occurrence Matrices (GLCMs) have the better performance. The LNC estimation of using fusion images was improved more obviously than multispectral those, and there was the best estimation at jointing stage based on Lasso Regression (LR), with R-2 of 0.66 and RMSE of 11.96%. Gaussian Process Regression (GPR) algorithm used in combination with one feature-screening method of Minimum Redundancy Maximum Correlation (mRMR) for the fusion images, showed the better improvement to LNC estimation, with R-2 of 0.68 and RMSE of 11.45%. It indicates that the information fusion from UAV multi-sensor imagery can significantly improve crop LNC estimates and the combination with multiple types of features also has a great potential for evaluating LNC in crops.

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