Evaluation of a hybrid AI model for the automatic identification of pollen morphological features for apitherapy applications

Authors : Uğur Şevik

Pages : 268-294

Doi:10.35206/jan.1826463

View : 76 | Download : 109

Publication Date : 2025-12-31

Article Type : Research Paper

Abstract :Melissopalynology is the gold standard for authenticating honey but traditional microscopic analysis is time-consuming and subjective. This study evaluates a hybrid artificial intelligence approach to automate pollen classification using the comprehensive POLLEN73S dataset, which features 73 distinct pollen types from the Brazilian Savanna. To address class imbalance, the dataset was expanded to 7300 images using data augmentation. We extracted morphological features using three pre-trained deep learning models (ResNet50, EfficientNetB0, MobileNetV2) and classified them using 17 traditional machine learning algorithms. The hybrid model combining ResNet50 features with Linear Discriminant Analysis (LDA) achieved the highest accuracy of 97.00%. Error analysis indicated that misclassifications were concentrated among taxonomically similar genera, such as Serjania, due to shared exine structures. These results demonstrate that the proposed hybrid model offers a highly accurate and scalable solution for laboratory-based honey authentication, provided it is integrated with debris detection systems to handle real-world samples.
Keywords : Melissopalinoloji, Polen Morfolojisi, POLLEN73S, Bal Kimlik Doğrulaması, Hibrit Sınıflandırma, ResNet50, Doğrusal Diskriminant Analizi

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