A Biologically Informed Wavelength Extraction (BIWE) Method for Hyperspectral Classification of Olive Cultivars and Ripening Stages

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Reliable tools for cultivar discrimination and ripening stage evaluation are critical to optimize harvest timing and support milling process focused on olive oil quality. This research examines the spectral properties of olive drupes throughout different maturation stages, ranging from green to full purple-black pigmentation, across 29 distinct cultivars. High-resolution spectrometric analysis was conducted within the 380–1080 nm wavelength range. Multiple analytical approaches were employed to optimize wavelength selection from hyperspectral reflectance data to obtain discriminating tools for olive classification. A Biologically Informed Wavelength Extraction method (BIWE) was developed, focusing on cultivar and ripening stages identification, and pivoted on biologically informed single wavelengths and Vegetation Indices (VIs) selection. The methodology integrated multi-scale spectral analysis with biochemically weighted scoring and a multi-criteria evaluation framework, employing a two-iteration refinement process to identify optimal spectral features with high discriminatory power and biological relevance. Analysis revealed spectral variations associated with maturation. A characteristic reflectance peak at approximately 550 nm observed during early ripening stages underwent a notable shift, developing into distinct spectral behavior within the 700–780 nm range in intermediate and advanced ripening stages and reaching a plateau for all the samples between 800 and 950 nm. The BIWE method achieved exceptional efficiency in olive classification, utilizing only 25 single wavelengths compared to 114 required by Principal Component Analysis (PCA) and 131 by Recursive Feature Elimination (RFE), representing 4.6-fold and 5.2-fold reductions, respectively. Despite this reduction, BIWE’s overall accuracy (0.5634) remained competitive compared to RFE (−10%) and PCA (−8%) alternative approaches requiring larger wavelengths dataset acquisition. The integration of biochemically relevant VIs enhanced accuracy across all methodologies, with BIWE demonstrating notable improvement (+19.2%). BIWE demonstrated effective olive identification capacity with a reduction in required wavelengths and VIs dataset, affecting the technological needs (spectrometer offset and real-time classification applications) for a tool oriented to olive cultivars and ripening stage discrimination.

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