Figion — Dried Fig Aflatoxin Analysis
Quality control and analysis based on aflatoxin levels
About the Project
Aflatoxins are highly toxic and carcinogenic compounds produced by Aspergillus mold species, posing a serious risk to food safety. Their formation in foodstuffs is primarily driven by inadequate environmental conditions, such as high humidity and inappropriate storage temperatures. While high-precision laboratory analyses are used for segregation, this technique creates significant operational constraints in large-scale commercial settings due to its high cost and time-consuming nature. This project aims for the rapid, low-cost, and automatic detection of aflatoxin-contaminated dried figs through image analysis under UV light. The yellowish fluorescence exhibited by contaminated areas under UV light forms the basis of this method. With the aid of a prepared dataset, accurate classification of healthy and aflatoxin-contaminated figs will be ensured, accelerating pre-laboratory screening and reducing operational costs.
- Goal: Rapid, low-cost, automatic detection of contaminated dried figs
- Method: UV-light imaging and computer vision to detect fluorescence
- Impact: Accelerated pre-lab screening and reduced operational costs
Team — Figion
Interdisciplinary collaboration and shared ownership.
👥 Team Members
👨🏫 Advisor
⚖️ Jury Members
Partners & Collaborators
Visual Analysis
Pipeline: UV fluorescence detection → background removal → defect detection → color-space metrics → aflatoxin risk classification.
- Batch processing with reproducible parameters
- Export JSON metrics + overlay images
Project Backlog
Track our progress through development sprints.
| Task Name | Priority | Status | Pts | |||
|---|---|---|---|---|---|---|
Sprint 1: Proposal & Specifications completedDefine project scope, constraints, and initial requirements for Figion. | ||||||
Submit Project Proposal (D1) | High | Complete | 5 | |||
Literature Review: UV Imaging & Aflatoxin | Medium | Complete | 8 | |||
Project Specifications Report (D2) | High | Complete | 13 | |||
Initial Backlog Document (D3) | Medium | Complete | 3 | |||
Launch Project Website | Low | Complete | 5 | |||
Sprint 2: System Analysis completedAnalyze requirements for the UV imaging and deep learning pipeline. | ||||||
Project Analysis Report (D4) | High | Complete | 13 | |||
Define Functional Requirements | High | Complete | 8 | |||
Update Backlog Document (D5) | Medium | Complete | 3 | |||
Sprint 3: High-Level Design completedDesign system architecture, subsystem decomposition, and hardware/software mapping. | ||||||
High-Level Design Report (D6) | High | Complete | 13 | |||
Design Subsystem Decomposition | Medium | Complete | 8 | |||
Update Backlog Document (D7) | Low | Complete | 2 | |||
Sprint 4: Final Presentation completedShowcase the Figion prototype and present results at the Year-end Exhibition. | ||||||
Oral Presentation (D8) | High | Complete | 8 | |||
Poster Presentation | Medium | Complete | 5 | |||
Final Prototype Demonstration | High | Complete | 21 | |||
Sustainable Development Goals
The project is framed through food safety and sustainable agriculture.