PalmOilRipenessDetection-Python

README.md

Palm Oil Ripeness Agent (n8n + YOLOv8)

This project uses a custom-trained YOLOv8 model to detect the ripeness of Palm Oil Fresh Fruit Bunches (FFB). It features a local Python FastAPI server that integrates into an agentic n8n workflow, storing results and embeddings in MongoDB Atlas.

🚀 Project Overview

1. Model: YOLOv8 Nano (custom-trained on Roboflow dataset).
2. Server: FastAPI (Python) hosting the model for inference.
3. Database: MongoDB Atlas (Vector Search for historical similarity).
4. Orchestration: n8n (Agentic workflow for decision making).

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🛠 Prerequisites

• Python 3.10+
• n8n (Desktop or Self-hosted)
• MongoDB Atlas Account (with Vector Search index enabled)
• Optional: NVIDIA GPU with CUDA for faster training.

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📦 Setup Instructions

1. Clone & Environment

git clone <your-repo-url>
cd palm-oil-ai
python -m venv venv
# Windows: venv\Scripts\activate | Mac: source venv/bin/activate
pip install -r requirements.txt

2. Dataset Preparation

1. Download the dataset from Roboflow Universe.
2. Unzip into the /datasets folder.

3. Ensure your data.yaml matches the local paths:

   train: ../datasets/train/images
   val: ../datasets/valid/images
   
   

   3. Training the Model

   To train locally without hanging your PC, use the throttled script:

   python train_script.py
   
   

• Outputs: The best model will be saved at runs/detect/train/weights/best.pt.
• Move it: Copy best.pt to the root directory for the server to use.

4. Running the Inference Server

python main.py

The server will start at http://localhost:8000.

• Endpoint: POST /detect
• Payload: Multipart Form-data (Key: file, Value: image.jpg)

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🤖 n8n Integration

The n8n workflow follows this logic:

1. Trigger: Receives image (Telegram/Webhook).
2. HTTP Request: Sends image to localhost:8000/detect.
3. MongoDB Node: Performs Vector Search using the returned embedding.
4. Agent Logic: Final ripeness determination based on model confidence + DB similarity.

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📂 Repository Structure

├── datasets/           # Labeled images from Roboflow
├── runs/               # YOLO training logs and weights
├── main.py             # FastAPI Inference Server
├── train_script.py     # Local training configuration
├── best.pt             # The "Brain" (Trained Model)
├── requirements.txt    # dependencies
└── README.md           # You are here

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📝 Future Improvements

• Implement CLIP embeddings for higher-accuracy vector similarity.
• Add a Streamlit dashboard for manual batch verification.