Introduction

The Color Bottle Detection System using Machine Learning is designed to identify and classify bottles based on their colors using image processing and computer vision. The system analyzes images captured by a camera and predicts the bottle's color. This project demonstrates how AI-based color detection can support automated sorting, quality inspection, and industrial applications.

Objectives

• To develop an ML-based bottle color detection system.

• To identify different bottle colors automatically.

• To apply image processing and computer vision.

• To reduce manual color identification.

• To support automated bottle sorting.

• To evaluate detection accuracy.

Methodology

• Collecting images of bottles with different colors.

• Preparing and labeling the image dataset.

• Training or configuring a suitable detection model.

• Integrating a camera for image capture and analysis.

• Testing the system under different lighting conditions.

Instrument List

• Computer or Laptop

• Camera or Webcam

• Colored Bottle Image Dataset

• Python Programming Language

• OpenCV Library

• TensorFlow / PyTorch (if ML training is used)

• Trained Model

• User Interface (if used)

Working Principle

• The camera captures an image of a bottle.

• The system processes the image to identify the bottle region.

• The model analyzes color-related features and classifies the bottle.

• The detected color is displayed on the screen.

• The classification result can be used for automatic bottle sorting if connected to a sorting mechanism.

Advantages

• Automatic Color Detection

• AI-Based Image Analysis

• Fast Bottle Classification

• Reduced Manual Sorting

• Improved Sorting Efficiency

• Supports Industrial Automation

• Contactless Detection

• Practical Computer Vision Application

Conclusion

The Color Bottle Detection System demonstrates how machine learning and image processing can identify bottle colors automatically. It can support bottle classification and provide a foundation for automated sorting applications. With proper training data, consistent lighting, and further integration, the system can be adapted for practical industrial use.