Inversion of nitrogen and phosphorus contents in cotton leaves based on the Gaussian mixture model and differences in hyperspectral features of UAV

文献类型： 外文期刊

第一作者： Peng, Lei

作者： Peng, Lei;Xin, Hui-Nan;Lv, Cai-Xia;Li, Na;Li, Yong-Fu;Geng, Qing-Long;Chen, Shu-Huang;Lai, Ning

作者机构：

关键词： Gaussian mixture modeling; Spectral difference classification; Field trials; Machine learning

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.6； 五年影响因子：4.3 ）

ISSN： 1386-1425

年卷期： 2025 年 327 卷

页码：

收录情况： SCI

摘要： The nitrogen (N) and phosphorus (P) contents in cotton leaves can directly reflect growth conditions. Rapid and nondestructive acquisition of the N and P content in cotton leaves at the field scale is essential for rational fertilization strategies and precision agriculture. However, traditional direct destructive sampling in the field is performed at the sample point scale, which cannot rapidly obtain cotton leaf N and P content from the entire field. In this study, we propose that post-classification modeling based on differences in spectral features is beneficial for improving the prediction of N and P contents in cotton leaves. To test this hypothesis, we first used principal component analysis to downscale the hyperspectral data and then used Gaussian mixture modeling (GMM) to segment the hyperspectral data for spectral differences. The in-situ measured data was then combined with the random forest model to establish N and P prediction models for cotton leaves with spectral differences and full samples. Finally, the predictive model was utilized for leaf N and P spatial mapping of cotton in the field using UAV hyperspectral images as the input data. The results demonstrate that the spectral reflectance features of the different clusters classified by the GMM differ significantly in intensity and shape. The accuracy of the cotton leaf N and P prediction model based on the spectral differences was attributed to the full sample. The results validate the existence of spectral differences between crop leaf content by UAV hyperspectroscopy, and

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