MANUAL
COLONY
COUNTING
AI-POWERED
AUTO
DETECTION
Welcome — AI Open Innovation Challenge 2026
Microbiology
Automated Reader
Validate Colony Count Before You Submit Results
ISO-17025 Compliant AI Accuracy Verification
Technical Support & Consultation
Training Instances Dataset
Detection Classes (Colony, Bubble, Dust, Crack, Artifact)
17025 Compliant Audit Standards
Computer Vision Architecture
Training Instances Dataset
Detection Classes (Colony, Bubble, Dust, Crack, Artifact)
17025 Compliant Audit Standards
Computer Vision Architecture
About ColonyAI Lab
The most advanced AI-powered laboratory automation solution custom-designed for the Healthcare Case 1 challenge in the AI Open Innovation Challenge 2026.
From Petri Dish
to Digital Report
ColonyAI captures petri dish images via laboratory nodes and runs them through a YOLOv8 multi-class detection model that identifies and counts bacterial colonies alongside artifacts such as bubbles, dust, and cracks.
Results are automatically compiled into ISO-17025 compliant audit reports with zero-trust security protocols, enabling real-time laboratory monitoring and traceability.
Case 1 — Microbiology Laboratory: Automated Plate Count Reader
AI Open Innovation Challenge 2026 · TUV NORD Indonesia
Brief Explanation
Microbiology labs perform Total Plate Count (TPC) tests to determine microorganisms in food and environmental samples. Analysts count colonies manually — making results time-consuming, inconsistent, and prone to error.
The Challenge
- Identify agar plate area from image
- Auto-detect & count bacterial colonies
- Differentiate colonies vs. artifacts (bubbles, dust, cracks)
- Produce consistent CFU/ml values
- Save results to laboratory reporting system
Scope & Limitations
- Variations in lighting & camera quality
- Overlapping and low-contrast colonies
- Different media types and colors
- Limited labeled dataset
- Results still require analyst verification
Expected Output
Model
Computer vision colony detection & counting
Dashboard
Colony count results and test history
Simulator
Comparison of manual vs AI accuracy
Exec. Summary
Efficiency of analysis time & consistency
ColonyAI's Solution
All 5 Challenge Criteria Addressed — YOLOv8 · ISO-17025 · Zero-Trust Security
Contact Us for Queries and
Assistance
An Initiative By
Organized By
Our Strategic Partners
Frequently Asked Questions
What is ColonyAI?
ColonyAI is an automated computer vision-based microbiology analysis platform developed to assist researchers and lab analysts in identifying and counting bacterial colonies with high precision.
How do I access the Neural Center?
You can access the Neural Center via the main dashboard after authorizing your laboratory node. Use the 'Neural Center' menu in the navigation to begin analysis.
Does this platform support ISO standards?
Yes, ColonyAI is designed to comply with ISO-17025 standards for laboratory data management and audit trails.
What if I need technical assistance?
Our support team is available 24/7 via hotline 0813-948-290 or via the chat widget in the lower right corner of the page.