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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.

UV Imaging
Yellowish fluorescence detection under UV light
Dataset & Classification
Healthy vs. contaminated fig labeling and modeling
Rapid Screening
Pre-lab triage to save time and cost
Automation
Low-cost, scalable, and automatic analysis pipeline
  • 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: 5 • Advisor: 1 • Jury: 2

👥 Team Members

Berk Kaya
Berk Kaya
Project Member
Onur Turan
Onur Turan
Project Member
Alperen Aktaş
Alperen Aktaş
Project Member
İrem Ayça Uçankale
İrem Ayça Uçankale
Project Member
İlhan Ün
İlhan Ün
Project Member

👨‍🏫 Advisor

Emin Kuğu
Emin Kuğu
Advisor

⚖️ Jury Members

Tolga Kurtuluş Çapın
Tolga Kurtuluş Çapın
Jury Member
Ayşe Yasemin Seydim
Ayşe Yasemin Seydim
Jury Member

Partners & Collaborators

Analysis Reports

Aflatoxin analysis and quality control reports.

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.

Sprint 1: Proposal & Specifications

Define project scope, constraints, and initial requirements for Figion.

COMPLETED
Submit Project Proposal (D1)Team
Literature Review: UV Imaging & AflatoxinTeam
Project Specifications Report (D2)Team
Initial Backlog Document (D3)Team
Launch Project WebsiteBerk

Sprint 2: System Analysis

Analyze requirements for the UV imaging and deep learning pipeline.

COMPLETED
Project Analysis Report (D4)Team
Define Functional RequirementsTeam
Update Backlog Document (D5)Team

Sprint 3: High-Level Design

Design system architecture, subsystem decomposition, and hardware/software mapping.

CURRENT
High-Level Design Report (D6)Team
Design Subsystem DecompositionTeam
Update Backlog Document (D7)Team

Sprint 4: Final Presentation

Showcase the Figion prototype and present results at the Year-end Exhibition.

UPCOMING
Oral Presentation (D8)Team
Poster PresentationTeam
Final Prototype DemonstrationTeam

Sustainable Development Goals

The project is framed through food safety and sustainable agriculture.

Good Health and Well-Being (SDG 3)Industry, Innovation and Infrastructure (SDG 9)Partnerships for the Goals (SDG 17)Decent Work and Economic Growth (SDG 8)
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