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Summary

This summary is machine-generated.

Artificial intelligence (AI) struggles with pollen identification due to trait variation. This study reveals spatial and temporal variations significantly impact AI accuracy, emphasizing the need for diverse data for robust pollen identification.

Keywords:

interspecific variation intraspecific variation machine learning multispectral image‐based flow cytometer pollen analysis reference database spatial and temporal variation

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Area of Science:

Botany
Computer Science
Data Science

Background:

AI excels at object recognition but faces challenges in identifying pollen grains.
Pollen trait variation, often overlooked in classical studies, limits machine learning applications.
Existing pollen databases lack sufficient variation for real-world AI performance.

Purpose of the Study:

To investigate spatial and temporal variations in pollen traits (morphology and fluorescence).
To understand how pollen variability affects AI classification accuracy.
To determine optimal data strategies for robust AI-based pollen identification.

Main Methods:

Analyzed 64,001 pollen grains from four plant species (Achillea millefolium, Lamium album, Lathyrus vernus, Lotus corniculatus).
Collected samples across four years and seven locations in Central Germany.
Utilized multispectral imaging flow cytometry for trait measurement.

Main Results:

Observed significant species-specific spatial and temporal trait variations in pollen.
Demonstrated that pollen variability and sample identity influence AI classification accuracy.
Found that multiple measurements from diverse origins yield the most robust AI identifications.

Conclusions:

Spatial and temporal variations are critical factors in pollen trait diversity.
AI-based pollen identification requires comprehensive datasets accounting for observed variability.
Diverse, multi-origin pollen data is essential for accurate and reliable AI classification.