🐛 Automated Cocoon Quality Assessment and Silk Yield Estimation

An AI-powered cocoon quality assessment system that automates silk cocoon grading and yield estimation using a two-stage deep learning pipeline:

1. YOLOv8 – for cocoon detection and segmentation
2. EfficientNet-B0 – for fine-grained classification into 6 quality categories

Includes a Renditta-based silk yield estimator and a Flask-based web interface for real-time industrial deployment.

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📌 Overview

Manual silk cocoon grading is slow, inconsistent, and subjective, with accuracy typically ranging from 70–80%.
This project leverages computer vision and deep learning to improve grading consistency, speed, and precision, achieving:

• 96.1% mAP@0.5 in cocoon segmentation
• 97% classification accuracy for quality grading

The system is suitable for industrial-scale silk factories and small farms, offering:

• Automated defect detection (double, pierced, crushed, decayed, yellow-spotted)
• Batch-level grading and yield forecasting
• Real-time web-based reporting

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🖼️ System Workflow

1. Data Acquisition

   • 3,460 cocoons photographed with high-resolution smartphone cameras
   • Final dataset: 3,068 annotated cocoons across 6 quality categories

2. Preprocessing & Augmentation

   • Resized to 640×640 pixels (YOLOv8) / 224×224 pixels (EfficientNet-B0)
   • Augmentations: mosaic, mixup, flips, rotations, brightness/contrast adjustments

3. Stage 1 – Segmentation (YOLOv8)

   • Detects and segments individual cocoons from batch images
   • Achieves high precision even under varying lighting and dense packing

4. Stage 2 – Classification (EfficientNet-B0)

   • Classifies each cocoon into one of six categories:
     • Good (Qualified)
     • Double, Crushed, Decayed, Pierced, Yellow-spotted (Defective)

5. Yield Estimation

   • Uses industry-standard Renditta formula to estimate silk production from batch weight and quality distribution

6. Deployment

   • Flask backend + HTML/CSS/JavaScript frontend for real-time use
   • Generates detailed batch reports with grading and yield outputs

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🏗️ Project Structure

Cocoon_Classification/
├── app.py                 # Flask backend for model inference and API
├── app.js                 # Frontend logic (AJAX requests to backend)
├── index.html             # Web interface for uploading and viewing results
├── style.css              # Frontend styling
├── best_classifier.pth    # Trained EfficientNet-B0 classification model
├── best_s_300.pt          # Trained YOLOv8 segmentation model
├── uploads/               # Directory for uploaded cocoon images
└── requirements.txt       # Python dependencies

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📊 Model Performance

Segmentation (YOLOv8)

• mAP@0.5: 96.1%
• Precision: 98.3%
• Recall: 94.9%
• F1-score: 96.6%

Classification (EfficientNet-B0)

• Accuracy: 97%
• Defective Precision/Recall: 100% / 93%
• Qualified Precision/Recall: 94% / 100%

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🚀 Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/omrajbhalke/Cocoon_Classification.git
cd Cocoon_Classification

2️⃣ Install Python dependencies

pip install -r requirements.txt

3️⃣ Download/verify trained models

Ensure best_s_300.pt and best_classifier.pth are present in the root folder. (If not, download from release assets or contact the maintainers.)

4️⃣ Run the application

python app.py

The backend will start at http://localhost:5000.

5️⃣ Access the frontend

Open index.html in your browser or serve it via a local server.

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📂 Dataset

• Total images: 3,068 cocoons
• Categories:
  • Good – 773 samples
  • Double – 468 samples
  • Crushed – 681 samples
  • Decayed – 474 samples
  • Pierced – 207 samples
  • Yellow-spotted – 465 samples
• Annotated using Roboflow for YOLOv8 segmentation training

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🛠️ Technologies Used

• Python 3.x
• YOLOv8 – segmentation
• EfficientNet-B0 – classification
• PyTorch – deep learning framework
• Roboflow – dataset labeling & augmentation
• OpenCV – image preprocessing
• Flask – backend API
• HTML, CSS, JavaScript – frontend

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📄 Silk Yield Estimation

The system predicts silk yield using Renditta:

Silk Yield (kg) = Total Cocoon Weight (kg) / Effective Renditta

Grades batches as:

• Grade A: > 85% qualified cocoons
• Grade B: 70–84%
• Grade C: 50–69%
• Substandard: < 50%

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📜 License

This project is licensed under the MIT License – see the LICENSE file for details.

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👥 Contributors

• Omraj Ravindra Bhalke, Aryan Vats, Krrish Agrawal, Aditya Singh

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📬 Contact

For queries or collaboration:

• Email: omrajbhalke245@gmail.com
• GitHub: @omrajbhalke

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