Inversion modeling of rice chlorophyll content based on optimized UAV hyperspectral remote sensing image data

Abstract

Chlorophyll content is an important indicator of rice growth status. Accurately estimating the nutritional status of rice canopies using hyperspectral data and inversion models is of great significance for precision farming. Ground-based spectrometers can obtain precise spectral data, but they are limited by spatial resolution and cannot be used for large-scale observations. Unmanned aerial vehicle (UAV) imaging spectrometers can be used to observe large areas of farmland, but due to sensor limitations, the data accuracy is poor, which in turn leads to poor inversion accuracy. This study optimizes UAV hyperspectral data based on ground-based spectrometer data. A wavelength random combination traversal algorithm is used to select wavelengths. Inversion models for rice chlorophyll content are constructed using ELM, CPO-ELM, and FLA-ELM. The results indicated that the optimized hyperspectral reflectance was highly consistent with ASD reflectance. The root mean square error of reflectance (RMSEReflectance) across all bands decreased from 0.108 to 0.012 (88.89 % reduction), and the average RMSEReflectance across all samples decreased by 88.64 %. Compared to the original UAV data, the optimized data achieved the best inversion performance with the FLA-ELM model: the coefficient of determination (R²) of the test set increased from 0.608 to 0.755 (24.2 % relative improvement), and the RMSE of chlorophyll content (RMSEChl) decreased from 6.518 μg/cm2 to 5.371 μg/cm2. These results validate the effectiveness of the proposed spectral optimization scheme. In conclusion, modeling based on optimized UAV hyperspectral imagery improves the accuracy of rice chlorophyll content inversion, providing a novel approach for rice nutritional monitoring.

Plant phenotyping relevance

UAVハイパースペクトル画像からイネのクロロフィル含量を推定する手法を最適化し、精度を定量的に検証しており、植物フェノタイピング手法が中心である。

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