AI Fruit Freshness & Condition Prediction System
Deep learning Computer Vision system utilizing CNN / ResNet architectures to classify fruit freshness grades and detect surface spoilage in real-time.
### AI Fruit Condition & Quality Grading System (FYP Complete Package)
The **AI Fruit Condition Prediction System** is a state-of-the-art computer vision engineering project designed to automate agricultural sorting and retail quality assurance. By training a deep Convolutional Neural Network (CNN / ResNet) on thousands of multi-class fruit samples (Apples, Bananas, Oranges, Tomatoes), this system accurately grades fruits into **Fresh vs. Rotten** states with high-confidence probability scores.
#### 🧠 Architecture & Tech Stack:
- **Core AI Framework**: Python 3.10+, PyTorch / TensorFlow, OpenCV
- **Model Architecture**: Transfer Learning with MobileNetV2 & ResNet-50 (>96% validation accuracy)
- **Web Application**: Django 5 / Flask with Bootstrap 5 responsive UI
- **Input Channels**: Live Webcam Stream, Image File Upload, and Batch Directory Processing
- **Reporting Engine**: Automated PDF inspection certificates with timestamped grading statistics
#### 📦 Complete FYP Deliverables Included:
- Complete, bug-free Python / Django Source Code
- Pre-trained Deep Learning Model Weights (`.h5` / `.pt` / `.onnx`)
- Curated & labeled 5,000+ Image Dataset with Data Augmentation scripts
- Comprehensive 50+ page Final Year Project Documentation Report (IEEE format)
- Professional Defense Presentation Slides (PowerPoint PPTX)
- Circuit schematics for optional conveyor belt sorting integration
What's Included in the Package:
- Complete Python & Django Source Code
- Pre-trained CNN & ResNet Model Weights
- Curated 5,000+ Image Fruit Quality Dataset
- Comprehensive IEEE Project Report (Word DOC & PDF)
- Project Defense Presentation Slides (PPTX)
- Video Setup & Installation Guide
Key Features
Specifications
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