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Related Experiment Video

Updated: Apr 15, 2026

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Intensity-Texture Enhanced Swin Fusion for Bacterial Contamination Detection in Alocasia Explants.

Jiatian Liu1, Wenjie Chen1, Xiangyang Yu1,2,3

  • 1State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary

This study introduces a new method for automatically detecting bacterial contamination in plant tissue cultures using multispectral imaging. The developed Intensity-Texture enhanced Swin Fusion (ITSF) model significantly improves detection accuracy for automated micropropagation.

Keywords:
Swin Transformerbacterial contaminationdeep learningimage fusionmultispectral imageplant tissue culture

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Area of Science:

  • Agricultural Science
  • Biotechnology
  • Computer Vision

Background:

  • Automated detection of bacterial contamination is crucial for efficient plant tissue culture and quality control.
  • Current methods often lack the precision required for industrial-scale micropropagation.

Purpose of the Study:

  • To develop a non-destructive, automated system for detecting bacterial contamination in Alocasia explants.
  • To propose and validate a novel image fusion model for enhanced bacterial detection.

Main Methods:

  • Development of a multispectral image acquisition system for Alocasia explants.
  • Implementation of the Intensity-Texture enhanced Swin Fusion (ITSF) model using convolutional neural networks and Swin Transformers.
  • Formulation of a composite loss function integrating texture loss, SSIM, and intensity-aware guided loss.

Main Results:

  • The ITSF model effectively extracts texture and intensity features from visible and near-infrared channels.
  • The fusion model successfully models long-range spatial dependencies and integrates cross-domain features.
  • Achieved a mean Average Precision (mAP50) of 0.949, demonstrating superior bacterial detection performance.

Conclusions:

  • The proposed ITSF method significantly enhances bacterial visual saliency and detection accuracy.
  • The system meets industrial requirements for high-precision inspection in automated micropropagation.
  • This provides a critical technical solution for the industrialization of plant tissue culture.