Artificial Intelligence in Honey Pollen Analysis: Accuracy and Limitations of Pollen Classification Compared with Palynological Expert Assessment

Honey authenticity, including its botanical origin, is traditionally assessed by melissopalynology, a labour-intensive and expert-dependent method. This study reports the final validation of a deep learning model for pollen grain classification in honey, developed within the NUTRITECH.I-004A/22 project, by comparing its performance with that of an independent palynology expert. A dataset of 5194 pollen images was acquired from five unifloral honeys, rapeseed (Brassica napus), sunflower (Helianthus annuus), buckwheat (Fagopyrum esculentum), phacelia (Phacelia tanacetifolia) and linden (Tilia cordata), under a standardized microscopy protocol and manually annotated using an extended set of morphological descriptors (shape, size, apertures, exine ornamentation and wall thickness). The evaluation involved training and assessing a deep learning model based solely on the ResNet152 architecture with pretrained ImageNet weights. This model was enhanced by adding additional layers: a global average pooling layer, a dense hidden layer with ReLU activation, and a final softmax output layer for multi-class classification. Model performance was assessed using multiclass metrics and agreement with the expert, including Cohen’s kappa. The AI classifier achieved almost perfect agreement with the expert (κ ≈ 0.94), with the highest accuracy for pollen grains exhibiting spiny ornamentation and clearly thin or thick walls, and lower performance for reticulate exine and intermediate wall thickness. Misclassifications were associated with suboptimal image quality and intermediate confidence scores. Compared with traditional melissopalynological assessment (approx. 1–2 h of microscopic analysis per sample), the AI system reduced the effective classification time to less than 2 min per prepared sample under routine laboratory conditions, demonstrating a clear gain in analytical throughput. The results demonstrate that, under routine laboratory conditions, AI-based digital palynology can reliably support expert assessment, provided that imaging is standardized and prediction confidence is incorporated into decision rules for ambiguous cases.