Estimation of Nitrogen Content in Hevea Rubber Leaves Based on Hyperspectral Data Deep Feature Fusion

Leaf nitrogen content is a critical quantitative indicator for the growth of rubber trees, and accurately determining this content holds significant value for agricultural management and precision fertilization. This study introduces a novel feature extraction framework—SFS-CAE—that integrates the Sequential Feature Selection (SFS) method with Convolutional Autoencoder (CAE) technology to enhance the accuracy of nitrogen content estimation. Initially, the SFS algorithm was employed to select spectral bands from hyperspectral data collected from rubber tree leaves, thereby extracting feature information pertinent to nitrogen content. Subsequently, a CAE was utilized to further explore deep features within the dataset. Ultimately, the selected feature subset was concatenated with deep features to create a comprehensive input feature set, which was then analyzed using partial least squares regression (PLSR) for nitrogen content regression estimation. To validate the effectiveness of the proposed methodology, comparisons were made against commonly used competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), and uninformative variable elimination (UVE) feature selection algorithms. The results indicate that SFS-CAE outperforms traditional SFS methods on the test set; notably, CARS-CAE achieved optimal performance with a coefficient of determination (R 2 ) of 0.9064 and a root mean square error (RMSE) of 0.1405. This approach not only effectively integrates deep features derived from hyperspectral data but also optimizes both band selection and feature extraction processes, offering an innovative solution for the efficient estimation of nitrogen content in rubber tree leaves.