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Articles

Automatic image segmentation as a possible key to detect contamination in in vitro plant tissue culture

Article number

1454_20

Pages

147 – 150

Language

English

Abstract

In vitro micropropagation is vital for modern biotechnology.
Maintaining sterile conditions is an essential pre-requisite, and early detection of fungal and bacterial contamination is crucial for explant recovery.
Artificial intelligence, particularly computer vision, offers a powerful tool for monitoring contamination.
This study presents a system utilizing a YOLOv11-based instance segmentation model for rapid analysis of in vitro cultures.
The system quantifies the contaminated area in images, enabling automatic assessment of contamination progression over time.
Data augmentation ensured good performance even with condensation or suboptimal lighting.
The system is designed for compatibility with compact cameras suitable for automation.

Publication

International Symposium on Biotechnological Tools in Horticulture

Authors

L. Capriotti, L. Nepi, A. Mancini, S. Sabbadini, F. Capocasa, R. Pierdicca, B. Mezzetti

Keywords

in vitro culture, contamination, image segmentation, computer vision, deep learning

Full text

https://doi.org/10.17660/ActaHortic.2026.1454.20

Groups involved

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