Development of an Edge-Computing-Based, Microcontroller-Integrated, Multi-Purpose, and Low-Cost Module: A Bacterial Colony Counting Case Study

Yıl 2024, Cilt: 14 Sayı: 2, 531 - 543, 01.06.2024

Yeliz Durgun , Mahmut Durgun

https://doi.org/10.21597/jist.1416788

Öz

This study aims to develop a low-cost, multi-purpose module based on edge computing for bacterial colony counting and classification. Due to the time-consuming and error-prone nature of traditional methods, this new system has been developed with microcontroller integration and artificial intelligence support. The system utilizes the Arduino Nano 33 BLE microcontroller and a 0.3MP OV7675 camera module, employing Gaussian Blur and Adaptive Thresholding techniques for image processing to better define bacterial colonies. The labeling and feature extraction of colonies involve analyzing characteristics such as area, perimeter, and density. Convolutional Neural Networks and Support Vector Machines have been used for bacterial colony counting and classification. The combination of these two algorithms has facilitated colony analysis. The developed system enables tracking of colony numbers and growth rates over time, emphasizing the importance of a microcontroller-integrated and AI-supported system in achieving fast and traceable results in bacterial colony counting and classification.

Anahtar Kelimeler

Image processing, Arduino Nano 33 BLE, Colony counting, Bacterial analysis

Kenar Hesaplama Tabanlı, Mikrodenetleyici Entegreli, Çok Amaçlı ve Düşük Maliyetli Modül Geliştirilmesi: Bakteriyel Koloni Sayımı Örneği

Öz

Bu çalışma, bakteriyel koloni sayımı ve sınıflandırması için edge-computing temelli, düşük maliyetli ve çok amaçlı bir modül geliştirmeyi amaçlamaktadır. Geleneksel koloni sayım yöntemleri zaman alıcı ve hata payı yüksek olduğundan, özellikle düşük yoğunluklu örneklerde doğruluk ve hassasiyet kaybına yol açar. Bu nedenle, mikrodenetleyici entegrasyonlu ve yapay zeka destekli bir sistem geliştirilmiştir. Çalışmada, Arduino Nano 33 BLE mikrodenetleyici ve 0.3MP OV7675 kamera modülü kullanılmıştır. Görüntü işleme süreçleri, bakteriyel kolonilerin segmentasyonu ve morfolojik işlemlerle daha iyi tanımlanması için Gaussian Blur ile Adaptif eşikleme teknikleri kullanılarak gerçekleştirilmiştir. Kolonilerin etiketlenmesi ve özellik çıkarımı için, alan, çevre ve yoğunluk gibi özellikler analiz edilmiştir. Bakteriyel koloni sayımı ve sınıflandırma işlemleri için Convolutional Neural Networks (CNN) ve Support Vector Machines (SVM) gibi iki farklı yapay zeka algoritması bir arada kullanılmıştır. CNN, görüntülerin doğrudan işlenmesi ve özellik çıkarımı için derin öğrenme tabanlı bir yöntemken, SVM çıkarılan özelliklere dayalı olarak sınıflandırma gerçekleştiren bir makine öğrenimi algoritmasıdır. Bu iki algoritmanın kombinasyonu, bakteriyel koloni analizinde kolaylık sağlamıştır. Geliştirilen sistem, bakteri kolonisi sayılarını ve büyüme hızını zamanla izlemeye olanak tanımaktadır. Bu çalışmanın sonuçları, bakteriyel koloni sayımı ve sınıflandırma süreçlerinde daha hızlı ve izlenebilir sonuçlar elde etmek için mikrodenetleyici entegrasyonlu ve yapay zeka destekli bir sistemin önemini vurgulamaktadır.

Anahtar Kelimeler

Arduino Nano 33 BLE, Bakteriyel koloni sayımı, Görüntü işleme, Mikrodenetleyici entegrasyonu

Kaynakça

• Albaradei, S. A., Napolitano, F., Uludag, M., Thafar, M., Napolitano, S., Essack, M., Bajic, V. B., & Gao, X. (2020). Automated counting of colony forming units using deep transfer learning from a model for congested scenes analysis. IEEE Access, 8, 164340–164346.
• Andreini, P., Bonechi, S., Bianchini, M., Mecocci, A., & Scarselli, F. (2018). A deep learning approach to bacterial colony segmentation. Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, 522–533.
• Aneja, K. R. (2007). Experiments in microbiology, plant pathology and biotechnology. New Age International. Bär, J., Boumasmoud, M., Kouyos, R. D., Zinkernagel, A. S., & Vulin, C. (2020). Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application. Scientific reports, 10(1), 16084.
• Chen, W.-B., & Zhang, C. (2009). An automated bacterial colony counting and classification system. Information Systems Frontiers, 11, 349–368.
• Choudhry, P. (2016). High-throughput method for automated colony and cell counting by digital image analysis based on edge detection. PloS one, 11(2), e0148469.
• Dönmez, S. İ., Needs, S. H., Osborn, H. M., Reis, N. M., & Edwards, A. D. (2022). Label-free 1D microfluidic dipstick counting of microbial colonies and bacteriophage plaques. Lab on a Chip, 22(15), 2820-2831.
• Durgun, Y. (2024). Classification of Starch Adulteration in Milk Using Spectroscopic Data and Machine Learning. International Journal of Engineering Research and Development, 16(1), 221-226. https://doi.org/10.29137/umagd.1379171
• Ferrari, A., Lombardi, S., & Signoroni, A. (2015). Bacterial colony counting by convolutional neural networks. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 7458–7461.
• Ferrari, A., Lombardi, S., & Signoroni, A. (2017). Bacterial colony counting with convolutional neural networks in digital microbiology imaging. Pattern Recognition, 61, 629–640.
• Hoffmann, S., Walter, S., Blume, A., Fuchs, S., Schmidt, C., Scholz, A., & Gerlach, R. (2018). High-Throughput Quantification of Bacterial-Cell Interactions Using Virtual Colony Counts. Frontiers in Cellular and Infection Microbiology, 8.
• Jin, S., Zeng, X., Xia, F., Huang, W., & Liu, X. (2021). Application of deep learning methods in biological networks. Briefings in bioinformatics, 22(2), 1902–1917.
• Karatepe, F., Taş, B., Coskun, O., & Kahriman, M. (2022). Detection of Escherichia Coli Bacteria by Using Image Processing Techniques. International Journal of Biology and Biomedical Engineering.
• Kis, B., Unay, M., Ekimci, G., Ercan, U., & Akan, A. (2019). Counting Bacteria Colonies Based on Image Processing Methods. 2019 Medical Technologies Congress (TIPTEKNO), 1-4.
• Liu, S., Gai, Z., Zhang, M., Guo, F., Chai, X., Wang, Y., Hu, D., Wang, S., Zhang, L., Zhang, X., Chen, Z., Sun, X., & Jiang, X. (2021). Small target detection method with high accuracy for visible colony RGB image formed by bacteria in water. , 11767, 117671D - 117671D-4.
• Lőrincz, Á. M., Szeifert, V., Bartos, B., & Ligeti, E. (2018). New flow cytometry-based method for the assessment of the antibacterial effect of immune cells and subcellular particles. Journal of Leukocyte Biology, 103(5), 955-963.
• Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of deep learning and reinforcement learning to biological data. IEEE transactions on neural networks and learning systems, 29(6), 2063–2079.
• Matsumoto, A., Schlüter, T., Melkonian, K., Takeda, A., Nakagami, H., & Mine, A. (2021). A versatile Tn7 transposon-based bioluminescence tagging tool for quantitative and spatial detection of bacteria in plants. Plant Communications, 3.
• Marotz, J., Lübbert, C., & Eisenbeiss, W. (2001). Effective object recognition for automated counting of colonies in Petri dishes (automated colony counting). Computer methods and programs in biomedicine, 66(2–3), 183–198.
• Melanthota, S. K., Gopal, D., Chakrabarti, S., Kashyap, A. A., Radhakrishnan, R., & Mazumder, N. (2022). Deep learning-based image processing in optical microscopy. Biophysical Reviews, 14(2), 463–481.
• Michal, Č., Radim, B., & Jan, K. (2022). Machine-learning Approach to Microbial Colony Localisation. 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 206–211.
• Naets, T., Huijsmans, M., Smyth, P., Sorber, L., & Lannoy, G. (2021). A Mask R-CNN approach to counting bacterial colony forming units in pharmaceutical development.
• Needs, S., Osborn, H., & Edwards, A. (2021). Counting bacteria in microfluidic devices: Smartphone compatible 'dip-and-test' viable cell quantitation using resazurin amplified detection in microliter capillary arrays.. Journal of microbiological methods, 106199 .
• Pacal, I. (2022). Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images. Journal of the Institute of Science and Technology, 12(4), 1917-1927.
• Pacal, I. (2023). Göğüs Röntgeni Görüntülerinden Otomatik COVID-19 Teşhisi için Görü Transformatörüne Dayalı Bir Yaklaşım. Journal of the Institute of Science and Technology, 13(2), 778-791.
• Pacal, I., & Alaftekin, M. (2023). Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları. Journal of the Institute of Science and Technology, 13(2), 760-777.
• Petersson, H., Gustafsson, D., & Bergstrom, D. (2016). Hyperspectral image analysis using deep learning—A review. 2016 sixth international conference on image processing theory, tools and applications (IPTA), 1–6.
• Qu, K., Guo, F., Liu, X., Lin, Y., & Zou, Q. (2019). Application of machine learning in microbiology. Frontiers in microbiology, 10, 827.
• Raju, S., Aparna, H., Krishnan, A., Naryanan, D., Gangadhran, V., & Paul, S. (2020). Automated counting of bacterial colonies by image analysis. Journal of multıdıscıplınary dental research.
• Rani, P., Kotwal, S., Manhas, J., Sharma, V., & Sharma, S. (2022). Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and developments. Archives of Computational Methods in Engineering, 29(3), 1801–1837.
• Shi, J., Zhang, F., Wu, S., Guo, Z., Huang, X., Hu, X., Holmes, M., & Zou, X. (2019). Noise-free microbial colony counting method based on hyperspectral features of agar plates.. Food chemistry, 274, 925-932 .
• Shousheng, L., Gai, Z., Xu, C., Fengxiang, G., Mei, Z., Xu, S., Yibao, W., Ding, H., Shaoyan, W., Zhang, L., Zhang, X., Chen, Z., Xiaoling, S., & Jiang, X. (2021). Bacterial colonies detecting and counting based on enhanced CNN detection method. E3S Web of Conferences.
• Signoroni, A., Savardi, M., Baronio, A., & Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. Journal of Imaging, 5(5), 52.
• Song, D., Liu, H., Dong, Q., Bian, Z., Wu, H., & Lei, Y. (2018). Digital, Rapid, Accurate, and Label-Free Enumeration of Viable Microorganisms Enabled by Custom-Built On-Glass-Slide Culturing Device and Microscopic Scanning. Sensors (Basel, Switzerland), 18.
• Şen Arslan, H., Cabi, A., Yerlikaya, S., & Sariçoban, C. (2021). Antibacterial and antioxidant activity of peach leaf extract prepared by air and microwave drying. Journal of Food Processing and Preservation, 45(10), e15847.
• Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., & Mittal, A. (2019). Pneumonia detection using CNN based feature extraction. 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT), 1–7.
• Veziroglu, E., Pacal, I., & Coşkunçay, A. (2023). Derin Evrişimli Sinir Ağları Kullanılarak Pirinç Hastalıklarının Sınıflandırılması. Journal of the Institute of Science and Technology, 13(2), 792-
• Yoon, S.-C., Lawrence, K. C., & Park, B. (2015). Automatic counting and classification of bacterial colonies using hyperspectral imaging. Food and bioprocess technology, 8, 2047–2065.
• Yerlikaya, S. (2021). Staphylococcus aureus ATCC 25923 inhibition with propolis in pasteurized and UHT milks. Journal of Agroalimentary Processes & Technologies, 27(3).
• Yerlikaya, S., Çiftçi, M., İşler, A., & Arslan, H. Ş. (2022). Determining antibacterial effect of yellow onion (allium cepa) peel extract on some pathogen inoculated in raw, uht and pasteurized milks. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 37(3), 707-716.
• Zhang, B., Zhou, Z., Cao, W., Qi, X., Xu, C., & Wen, W. (2022). A New Few-Shot Learning Method of Bacterial Colony Counting Based on the Edge Computing Device. Biology, 11.
• Zhang, J., Li, C., Rahaman, M. M., Yao, Y., Ma, P., Zhang, J., Zhao, X., Jiang, T., & Grzegorzek, M. (2022). A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artificial Intelligence Review, 1–70.
• Zhang, Y., Jiang, H., Ye, T., & Juhas, M. (2021). Deep learning for imaging and detection of microorganisms. Trends in Microbiology, 29(7), 569–572.