Comparison between neural networks and partial least squares for intra-growth ring wood density measurement with hyperspectral imaging

•Hyperspectral imaging allows measuring wood density at 79 μm spatial resolution.•Partial least squares or neural networks transform hyperspectral data to density.•Neural networks provide better results than partial least squares.•The mean absolute percentage error for neural networks is 6.49%.•Our method may substitute X-ray microdensitometry measurements.

In this paper, a procedure for transforming hyperspectral imaging information into intra-growth ring wood densities is presented. Particular focus was given to comparing the neural network and Partial Least Squares Regression (PLSR) processing methods. The hyperspectral measurements were performed in a wavelength range of 380–1028 nm, with a spatial separation of 79 μm. The study employed 34 samples from the same number of Pinus pinea tree samples. Density values were analyzed at a total of 34,093 positions in the samples. For neural networks, the mean absolute percentage error (MAPE) and standard deviation of absolute percentage error (StdAPE) values were 6.49% and 5.43%, respectively. For the PLSR method the MAPE and StdAPE were 6.87% and 5.70%, respectively. The neural networks allow reducing the percentage of sample positions with large errors. The proposed method for density measurement can be used for dendrochronology and dendroclimatology.