Comparing a Band Selection Algorithm with PCA Feature Extraction from Hyperspectral Reflectance for N Monitoring of Processing Tomato

Hyperspectral imaging adoption is hindered by high investment costs, and multicollinearity among contiguous bands requires dimensionality-reduction (DR) algorithms, raising concerns about cost-effectiveness for farmers. In this study, we propose a direct comparison between feature extraction and feature selection methods as DR strategies for monitoring the nitrogen (N) status of processing tomato. The hyperspectral reflectance (399–1001 nm) was monitored in a processing tomato experiment with four N levels alongside crop traits (aboveground biomass, AGB; N uptake; N nutrition index, NNI). Gaussian process regression (GPR) was trained in two DR scenarios, (i) feature extraction using principal component analysis (GPR-PCA) and (ii) feature selection, with a sequential backward band removal (band analysis tool, BAT, in ARTMO software (v3.30); GPR-BAT), to identify the most informative key bands. The GPR-PCA achieved the highest mean accuracy for AGB, whereas for N uptake and NNI GPR-BAT yielded comparable estimation performance. Selected bands were located in the NIR for all variables, in the red-edge for N uptake and NNI, and in the blue region for NNI, and were interpreted as candidate spectral regions. Based on the identified bands, two candidate multispectral sensor configurations were proposed for N monitoring: an AGB–NNI scenario (6 bands) and an AGB–N uptake scenario (5 bands). These findings suggest the potential of task-specific multispectral sensors for crop N monitoring, although NNI accuracy (RMSE ≈ 0.11 NNI units) remains insufficient for decisions close to the fertilization threshold.