Fruit Classification
6000 8000
You Save 25% (Inclusive of all taxes)
- Availibility: In Stock
Product Specification
- Product Name: Fruit Classification System
- Version: 1.0
- Platform: Python 3.x
- Hardware Requirements:
- Camera Module (for capturing fruit images)
- Raspberry Pi, PC, or similar computing device
- Software Requirements:
- Python 3.x
- OpenCV library for image processing
- TensorFlow/Keras or PyTorch (for machine learning model)
- Numpy library
- Flask (optional, for web interface)
- Functional Requirements:
- Real-time image capture and fruit detection
- Classification of fruits into different categories
- Display of classification results
- User-friendly interface for viewing and managing classification data
- Non-functional Requirements:
- High accuracy in fruit classification
- Low latency in processing
- Robustness and reliability in various lighting conditions
- Scalability for different types of fruits
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Description
Key Features:
-
Real-time Image Capture and Fruit Detection:
- The system captures images of fruits in real-time using a camera module.
- Detected fruits are processed for classification.
-
Fruit Classification:
- The system uses a pre-trained machine learning model to classify fruits into different categories.
- The classification model is trained on a dataset of various fruit images to achieve high accuracy.
-
Result Display:
- The system displays the classification results, including the type of fruit and confidence score.
- Results can be viewed in real-time on a display screen or through a web interface.
-
User Management:
- An administrative interface allows for managing the classification data and adding new fruit categories.
- Users can view detailed information about each classified fruit.
Additional information
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