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

Abstract

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.

Keywords

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

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

Abstract

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.

Keywords

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

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