Overview
This research developed an image-analysis platform for monitoring mushroom mycelial growth in liquid culture. The work focused on the king oyster mushroom ( Pleurotus eryngii ) under submerged cultivation conditions.
Conventional growth assessment relies on sampling and dry-weight measurement. Because that process is destructive and time-consuming, the project examined whether cultivation images could provide a practical way to measure growth between sampling points.
Research contribution
I worked on the pipeline that transformed cultivation imagery into quantitative measurements of growth.
- Image preprocessing and normalization.
- Detection of mycelial aggregates.
- Object tracking across cultivation time points.
- Measurement of aggregate size and count.
- Development of the AI-assisted monitoring workflow.
- Comparison of image-derived measurements with experimental observations during validation.
The measurement problem
During liquid cultivation, mushroom mycelia form visible aggregates or pellets rather than remaining as evenly suspended individual cells. Their irregular shapes, overlap, and changing sizes make growth difficult to represent with a single bulk measurement.
The vision system therefore needed to identify individual aggregates consistently and turn their visible changes into measurements that could be compared across cultivation conditions and time points.
Analysis pipeline
Video → image acquisition → preprocessing → aggregate detection → tracking → quantitative growth analysis
Cultivation images were captured over time and prepared for analysis. The system then identified visible aggregates and produced structured measurements of their count, size, and growth progression.
Experimental validation
The measurements were evaluated in cultivation experiments that varied carbon sources, nitrogen sources, and optimized conditions. Image-derived results were compared with experimental observations to assess whether they reflected the observed growth patterns.
The poster reports that the platform captured changes in mycelial growth and distinguished among the tested cultivation conditions.
Research details
Full title: Development of an AI-based Image Analysis Platform for Growth Monitoring of Mushroom Mycelia in Liquid Culture
Authors: Hyung Jin Cho, Stuart Asiimwe, Yun-A Shin, Kwang-Rim Baek, and Seung-Oh Seo
Affiliation: Department of Food Science and Biotechnology, Seoul National University of Science and Technology, Seoul, Republic of Korea
Citation:
Cho, H. J., Asiimwe, S., Shin, Y.-A., Baek, K.-R., & Seo, S.-O.
(2025).
Development of an AI-based Image Analysis Platform for Growth
Monitoring of Mushroom Mycelia in Liquid Culture
[Research poster].