PalmOilRipenessDetection-Python

Palm Oil Fruit Ripeness Recognition Service

A server-side application for identifying the ripeness of palm oil fruits using computer vision.

Overview

This project provides a recognition service for palm oil fruit ripeness. It is designed to assist in the automation of the harvesting process by providing accurate assessment of fruit maturity.

Features

• Ripeness Classification: Uses advanced image recognition to categorize palm oil fruits (e.g., Unripe, Under-ripe, Ripe, Over-ripe).
• API Interface: Built to serve mobile and web clients for real-time recognition.

Getting Started

Prerequisites

• Python 3.8+
• Virtual environment (recommended)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd palm-oil-ai
   

   1. Set up virtual environment: powershell python -m venv venv .\venv\Scripts\Activate.ps1

2. Install dependencies:

   pip install -r requirements.txt
   

   (Note: Ensure requirements.txt is created/updated as the project develops)

   Running the Server

   To start the recognition service:

   # Assuming a FastAPI/Uvicorn setup (common for such services)
   uvicorn main:app --reload
   

Dataset

This project uses the YOLOv8 dataset format for training and evaluation:

• YOLOv8 Dataset: oil palm ripeness.v5-roboflow-instant-2--eval-.yolov8

License

[License Type] - See LICENSE file for details.