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📘 Wheat Kernel FDK Detection and Analysis

Poster

🔍 Overview

This project provides a complete pipeline to automatically detect, classify, and analyze wheat kernels from image plates using a pre-trained YOLOv8 model. It is tailored to identify and quantify kernel traits such as:

  • Bleachness (based on average RGB intensity)
  • Roughness (based on standard deviation of RGB intensity)
  • Crease presence (based on profile shape analysis)

The script performs per-kernel analysis, per-plate summaries, and generates visualizations for statistical interpretation across multiple images.

🗂️ Directory Structure

.
├── input_images/              # Folder with test JPG images of plates
├── output/                   # Auto-generated results
│   ├── images/               # Labeled images + kernel grid images
│   ├── individual_kernels_csv/ # CSV files for each plate (per-kernel data)
│   └── plate_level_summary.csv # Final summary: one row per plate
├── wheat_kernel_FDK_Detector.pt  # Trained YOLOv8 model
├── FDK Detection - Poster - Final.pdf  # Poster
└── README.md            # Full pipeline script (this code)

⚙️ Setup Instructions

  1. Environment Requirements:
  • Create a conda environment:
conda create -n cvml python=3.12

pip3 install torch torchvision torchaudio
  • Install jupyterlab:
pip install jupyterlab
  • Install opencv:
pip install opencv-contrib-python
  • Install ultralytics:
pip install ultralytics
  • Install roboflow:
pip install roboflow==1.1.48
  1. Input Preparation:
    • Place all .jpg or .jpeg plate images in the input_images/ folder.
    • Make sure the model file wheat_kernel_FDK_Detector.pt is placed in the project root.

🚀 How It Works

  1. Object Detection with YOLOv8:

    • Kernels are detected and segmented from each image using a confidence threshold of 0.90.

  2. Image Masking and Cropping:

    • Each detected kernel is isolated using polygon masks and cropped for analysis.

  3. Kernel Rotation:

    • Kernels are aligned horizontally using ellipse fitting to normalize their orientation.

  4. Crease Classification:

    • Each rotated kernel is classified as With Crease or Without Crease based on profile length ratio using a smoothed horizontal intensity scan.

  5. RGB-Based Feature Extraction:

    • For each kernel:
      • Average Intensity (proxy for bleachness)
      • Standard Deviation (proxy for roughness)
    • Values are rounded and stored for CSV output.

  6. CSV Outputs:

    • A .csv file for each plate (individual_kernels_csv/) containing per-kernel stats.
    • A combined summary plate_level_summary.csv with average metrics for each plate.

  7. Visual Outputs:

    • Annotated plate images with numbered kernels
    • Grid images of crease vs non-crease kernels

  8. Statistical Histograms:

    • Plots generated:
      • % of bleached kernels
      • % of rough kernels
      • % of non-crease kernels
      • Total kernel count per plate

📊 Output Description

Per-Kernel CSV (one per plate):

Plate_Name Kernel_ID Average_Intensity Std_Intensity
example1 1 135 48

Summary CSV:

Plate_Name Average_Intensity Std_Intensity Total_Kernels
example1 132.5 47.3 104

🧪 Threshold Parameters

  • Average Intensity Threshold: 180 – Determines % of bleached kernels
  • Standard Deviation Threshold: 65 – Determines % of rough kernels
  • These thresholds can be adjusted via:
THRESHOLD_AVG_INTENSITY = 180
THRESHOLD_STD_INTENSITY = 65

📈 Visualization

The script generates histograms showing:

  • Distribution of bleachness across plates
  • Distribution of roughness
  • Kernel crease frequency
  • Kernel counts per plate

Use these plots to understand phenotypic variability and detect outliers.

📞 Contact

Prof. Jessica Rutkoski | jrut@illinois.edu

Prof, Sunoj Shajahan | sunoj@illinois.edu

Seung Wook Yoon | alexyoon9941@gmail.com, swyoon3@illinois.edu