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Updated: Sep 27, 2025

Collection and Identification of Pollen from Honey Bee Colonies
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Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset.

Nikos Tsiknakis1, Elisavet Savvidaki2, Georgios C Manikis1

  • 1Computational Biomedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas-FORTH, 70013 Heraklion, Greece.

Plants (Basel, Switzerland)
|April 12, 2022
PubMed
Summary

Deep learning models show high accuracy for classifying pollen types from images. However, models trained on general pollen datasets require further fine-tuning for effective honey pollen analysis.

Keywords:
classificationdeep learningensemblehoney certificationmelissopalynologypollen graintransfer learning

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Area of Science:

  • Botany
  • Computer Science
  • Food Science

Background:

  • Pollen identification is crucial for honey's botanical certification.
  • Manual microscopic analysis (melissopalynology) is labor-intensive and prone to errors.
  • Automated methods are needed to improve efficiency and consistency.

Purpose of the Study:

  • To evaluate deep learning models for classifying 20 pollen types using the Cretan Pollen Dataset.
  • To assess the performance of transfer and ensemble learning techniques for pollen classification.
  • To investigate the effectiveness of a trained model on real honey samples.

Main Methods:

  • Utilized transfer learning and ensemble methods on the Cretan Pollen Dataset.
  • Trained deep learning models for pollen grain image classification.
  • Performed a preliminary case study applying the best model to honey-based pollen images.

Main Results:

  • Achieved high performance on the Cretan Pollen Dataset: 97.5% accuracy, 96.9% sensitivity, 97% precision, 96.89% F1 score, and 0.9995 AUC.
  • The best-performing model showed poor results on honey-based pollen images (AUC of 0.52).

Conclusions:

  • Deep learning models, particularly with transfer and ensemble learning, are highly effective for classifying pollen types in controlled datasets.
  • Significant fine-tuning on honey-specific pollen data is necessary for practical application in honey analysis.
  • Further research should focus on adapting models for real-world melissopalynology challenges.