ACS Omega· 2026Q1
Deep-Learning-Assisted Single-Shot Plasma Emission Imaging for Pollen Morphology Reconstruction and Apparent Hydration-State Classification
- 0citations
- Q1SCImago
- 2026year
Short summary
A deep learning model reconstructs pollen morphology and classifies hydration state from single-shot plasma emission images, achieving ~76% accuracy in distinguishing hydrated from dehydrated grains.
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Key points
- Pollen morphology and hydration state can be inferred from plasma emission images generated by laser interaction.
- A conditional generative adversarial network reconstructs pollen morphology from plasma plumes.
- Chromaticity analysis of plasma plumes distinguishes hydrated from dehydrated pollen grains.
- A support vector machine achieved ~76% leave-one-out accuracy in hydration state classification.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Monitoring pollen is important for crop productivity, plant breeding, and environmental forecasting, yet current approaches remain limited in throughput and physiological insight. Here, we combine single-shot plasma emission imaging with artificial intelligence for pollen morphology reconstruction and apparent hydration state classification. Plasma plumes generated during laser–pollen interaction provide indirect measurements of pollen morphology, extending plasma-based analysis beyond conventional spectroscopic readouts. A conditional generative adversarial neural network reconstructed morphology from such plasma images, while chromaticity analysis revealed systematic color shifts between hydrated and dehydrated grains. A support vector machine using chromaticity histogram and scalar color features distinguished these groups with ∼76% leave-one-out accuracy. The results suggest that plasma emission contains structural and hydration-associated information, supporting rapid pollen morphology inference and physiological phenotyping for agricultural and environmental monitoring.
The authors' abstract, as published at the source. ACS Omega, 2026 · DOI ↗
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Field: Immunology and Allergy
Immunology and AllergyMedicine