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 Freshness & Condition Prediction System is an authentic, engineering-grade DIY project kit designed for prototyping, robotics, and academic research. Includes verified schematics, pre-tested hardware, and nationwide express shipping across Pakistan.
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
Quick Build & Interfacing Guide:
Unbox and verify all pre-tested sensors, microcontrollers, and wiring components included in your package.
Secure mechanical brackets, servos, and modules to the main frame using the provided mounting hardware.
Interface VCC, GND, and GPIO signal lines according to the verified schematic diagram provided with the kit.
Upload the tested source code via Arduino IDE, Python, or ESP-IDF and verify live sensor readings and actuation.
Need a Custom 3D Printed Casing or Multilayer PCB?
Get custom snap-fit enclosures and robotics parts printed via our custom 3D printing service in Lahore & Pakistan, or order industrial double-sided circuit boards through our custom PCB fabrication service with nationwide delivery.
Key Features
Specifications
Community Questions & Answers
Have a question about compatibility, code, or delivery? Ask our engineering team or see what other Pakistani makers asked.
No questions asked yet for this item
Be the first student or maker to ask a question!
