Image Processing Based Colony Identification Research
- 1 College of Mechanical Engineering, Guizhou Institute of Technology, Guiyang, China
- 2 College of Food and Pharmaceutical Engineering, Guizhou Institute of Technology, Guiyang, China
Abstract
Aiming at the shortcomings of the existing automatic colony counter, a set of algorithms based on the principle of image chromatic aberration to achieve colony identification is proposed, and a colony identification device is developed on this basis. The colony identification method is mainly based on the fact that different kinds of colonies and different concentrations of the same kind of colonies have different light-absorbing characteristics, and the judgement of colony types and concentrations is achieved through the method of image processing. The main features of the developed colony recognition equipment are high working efficiency, short recognition and detection time, and the potential of mixed recognition ability of multiple colonies. Therefore, the identification method and equipment have good application and promotion value in agriculture, food, medicine and other industries.
- Liu, X.M. (2022) Development of Plate Culture Medium for Simultaneous Counting of Coliform and Aerobic Plate Count and Study on Rapid Detection Method of Coliform. Master’s Thesis, South China University of Technology.
- Zhou, D.H., Zhao, X.K., Wang, J.Y., Guo, Z.Y. and Yang, Y. (2022) Comparison of Total Bacterial Count Between Easy Disc and Traditional Plate Counting Method. Biological Chemical Engineering , 8, 59-62.
- Chen, X.P., Sun, Y.M., Chen, Y.Y., et al. (2024) Research on Colony Segmentation and Counting Algorithm Based on Image Processing. China New Technology and New Products , No. 9, 29-31.
- Jiang, Y.X., Sun, M.H., Wang, T., Yan, W.W. and Ma, Y.W. (2023) Design of Colony Count Algorithm Based on Machine Vision. Manufacturing Automation , 45, 1-5.
- He, J.J., Li, Z.Y. and Ma, X.Y. (2024) Colony Segmentation and Counting Algorithm Based on Target Colour Base and Gradient Direction Matching. Acta Microbiologica Sinica , 64, 953-967.
- Cui, Q.S., Liu, Z.Z., Luo, G.L., et al. (2020) Development of Automatic Colony Counter Based on Artificial Intelligence. China Fiber Inspection , No. 12, 66-69.
- Sannidhan, M.S., Martis, E.J., Krivic, S., et al. (2023) A Swarm-Optimized Microbial Colony Counter. Expert Systems , 41, e13510. https://doi.org/10.1111/exsy.13510
- Bhuyan, S., Yadav, M., Giri, S.J., Begum, S., Das, S., Phukan, A., et al. (2023) Microliter Spotting and Micro-Colony Observation: A Rapid and Simple Approach for Counting Bacterial Colony Forming Units. Journal of Microbiological Methods , 207, Article ID: 106707. https://doi.org/10.1016/j.mimet.2023.106707
- Fan, X.Y. and Dai, Q. (2024) Research on Colony Counting Algorithm Based on Improved YOLOv5. Software Engineering , 27, 34-38.
- Sun, H.C., Hu, S. and Yan, W.W. (2023) Research on Colony Counting Based on Improved Otsu + Hough Image Processing Algorithm. Equipment Manufacturing Technology , No. 8, 7-10, 27.
- Gong, Y.T. and Wang, Y.G. (2024) Automatic Colony Counter Application of AI Image Recognition Technology. China Fiber Inspection , No. 6, 18-20.