Fractional order modeling and recognition of nitrogen content level of rubber tree foliage

The Nondestructive estimation method of nitrogen content level of rubber tree foliage was investigated utilizing nearinfrared (NIR) spectroscopy and Grunwald-Letnikov fractional calculus. Four models, including partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), extreme learning machine (ELM) and convolutional neural networks (CNN) are applied to construct the nitrogen estimation model. The results show that models established by 0.6-order or 1.6-order spectra achieved better performance than models with integer-order spectra. Afterward, the successive projections algorithm (SPA) is applied to reduce the number of variables, which is critical for developing portable nitrogen-level detector devices for rubber trees. The PLS-DA method achieved the best performance with an optimal recognition rate (97.73%) using the 1.6-order spectra. The results suggest that nitrogen content of rubber trees could be reliably estimated by fractional calculus processed NIR spectra. The method proposed here has a wide range of applicability and can provide more useful information for NIR spectral analysis in agriculture as well as other fields.

Automated summary

The nondestructive estimation of nitrogen content in rubber tree foliage was successfully achieved using NIR spectroscopy processed with fractional calculus. Among the models tested, PLS-DA performed the best with a recognition rate of 97.73% using 1.6-order spectra. This method has wide applicability and can provide valuable information for NIR spectral analysis in agriculture and beyond.