Abstract
Huanglongbing (HLB) is the most destructive disease affecting citrus production worldwide, causing severe yield losses and tree decline. Hyperspectral proximal sensing provides detailed information on leaf reflectance; however, its effectiveness for reliable HLB detection depends on the identification of consistent and discriminative spectral patterns. In this study, hyperspectral reflectance data (400–2450 nm) were combined with supervised machine learning algorithms to distinguish HLB-positive and HLB-negative citrus samples at two complementary scales: (i) leaf-level classification using individual spectral signatures and (ii) plant-level classification based on aggregated spectral responses. Model performance was evaluated under a leave-one-field-out validation framework designed to minimize spatial dependence between training and testing data. Results demonstrated consistently high classification performance, with Logistic Regression achieving accuracies above 0.94 at the leaf level and up to 1.00 at the plant level in several configurations. Dimensionality reduction using Principal Component Analysis (PCA) preserved most of the discriminative information, although with a slight reduction in recall under certain FIELD combinations. In contrast, Class balancing produced no observed improvement, whereas hyperparameter optimization showed limited and configuration-dependent effects in classification performance. Spectral coefficient analysis revealed that the most relevant wavelengths are concentrated in the visible (400–700 nm), red-edge (700–750 nm), near-infrared (750–1300 nm), and shortwave infrared (1500–2500 nm) regions, reflecting physiological alterations associated with pigment degradation, mesophyll disruption, and changes in water and biochemical composition. Overall, hyperspectral reflectance combined with parsimonious machine learning models enabled consistent discrimination of HLB-positive and HLB-negative samples within the evaluated orchard under validation designed to reduce spatial dependence, particularly at the plant scale.