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Updated: Apr 23, 2026

Collection and Identification of Pollen from Honey Bee Colonies

Collection and Identification of Pollen from Honey Bee Colonies

Published on: January 19, 2021

Automated pollen identification using microscopic imaging and texture analysis.

J Víctor Marcos¹, Rodrigo Nava², Gabriel Cristóbal¹

¹Institute of Optics, Spanish National Research Council (CSIC), Serrano 121, Madrid, Spain.

Micron (Oxford, England : 1993)

|September 27, 2014

Summary

This summary is machine-generated.

Automated pollen identification using texture analysis achieved 95% accuracy. Log-Gabor filters and discrete Tchebichef moments showed superior performance for pollen classification, aiding allergy prevention and apiculture.

Keywords:

Discrete Tchebichef moments Gray-level co-occurrence matrix Local binary patterns Log-Gabor filters Pollen identification Texture analysis

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Area of Science:

Botany
Computer Science
Image Processing

Background:

Pollen identification is crucial for allergy prevention, climate analysis, and apiculture.
Manual microscopic identification by experts is time-consuming and requires specialized knowledge.

Purpose of the Study:

To assess the effectiveness of texture analysis for automated pollen characterization and classification.
To compare different texture feature extraction methods for pollen image analysis.

Main Methods:

A database of 1800 brightfield microscopy images from 15 pollen taxa was utilized.
Four texture feature extraction methods were evaluated: Haralick's GLCM, LGF, LBP, and DTM.
Fisher's discriminant analysis and k-nearest neighbour were applied for dimensionality reduction and classification.

Main Results:

Log-Gabor filters (LGF) and discrete Tchebichef moments (DTM) outperformed GLCM and LBP.
Methods based on spectral properties (LGF, DTM) showed better performance.
Combining all texture features yielded the highest accuracy of 95%.

Conclusions:

Texture analysis is a viable approach for automated pollen recognition.
LGF and DTM are effective methods for pollen texture feature extraction.
Comprehensive texture characterization can enhance automated pollen identification systems.