Active Final Year Projects Showcase
Explore verified, ongoing final year projects advancing across all academic batches. Filter by emerging tech stacks, discover architectures, and explore student innovation.
Techrive innovative Finance Management
Techrive Innovative Finance Management System is a modern, web-based application designed to help individuals and businesses efficiently manage their financial activities. The system provides a smart and user-friendly platform for tracking ...
Context Based AI Assitant
The Context-Based AI Assistant is an intelligent desktop application designed to enhance human-computer interaction through natural language communication. Unlike traditional digital assistants that rely heavily on cloud services and offer ...
Intelligent camera based smart parking system
The Intelligent Camera-Based Smart Parking System is a mobile application that uses camera input and OCR (Optical Character Recognition) technology to automatically detect and manage parking information in real time. The system captures im...
Drivemart
Drive Mart is an innovative online platform designed to simplify the buying and selling of vehicles. The project aims to connect car buyers and sellers in a secure, efficient, and user-friendly environment. Through Drive Mart, users can bro...
Intelligent Fruit Ripeness Detection and Automated Counting using YOLO-based Computer Vision
In traditional agricultural and industrial practices, fruit ripeness is assessed manually based on visual inspection. This method is time-consuming, inconsistent, and prone to human error, leading to incorrect harvesting decisions, reduced product quality, and financial losses. Additionally, manual counting of fruits is inefficient, especially in large-scale environments such as farms, warehouses, and export units. Therefore, there is a need for an automated, accurate, and intelligent system that can detect fruits, determine their ripeness, and count them efficiently.
RemitChain: A Blockchain-Based Mobile App for Low-Cost Crypto Remittance to Pakistan
Pakistan receives over $30 billion in remittances annually, yet existing platforms like Western Union and Wise charge 3–7% in fees, rely on multiple intermediaries, cause slow processing times, and remain inaccessible to unbanked users creating a significant financial burden on overseas Pakistanis, freelancers, and low-income families who depend on frequent cross-border transactions.
Fraud Prevention & Verification System for Digital Micro-Transactions
What problem does it solve? Small-scale retailers face high risks of digital payment fraud, such as customers showing fake transaction screenshots. Manually opening banking applications to verify every small payment causes severe delays at...
An NLP-Based system for Automated Resume Generating and Intelligent hiring
Problem Statement In the modern job market, recruiters receive a large number of resumes, making manual screening time-consuming and inefficient. At the same time, many job seekers struggle to create professional resumes and present their ...
COMSATS University complain management system
1. Project Scope In-Scope: · Online complaint submission (text, attachments, category selection) · User registration & role-based login (Student, Faculty, Admin, Registrar) · Complaint status tracking (Submitted → Under Review → In ...
| Project Information | Status | Batch | Technologies | Action |
|---|---|---|---|---|
|
Techrive innovative Finance Management
Techrive Innovative Finance Management System is a modern, web-based application designed ...
|
Active | FA23 (2026) |
Node.js
Supabase
React
+1
|
Details |
|
Context Based AI Assitant
The Context-Based AI Assistant is an intelligent desktop application designed to enhance h...
|
Active | FA23 (2026) |
Python
JavaScript
PyTorch
+1
|
Details |
|
Intelligent camera based smart parking system
The Intelligent Camera-Based Smart Parking System is a mobile application that uses camera...
|
Active | FA23 (2026) |
Python
Dart
JavaScript
+8
|
Details |
|
Drivemart
Drive Mart is an innovative online platform designed to simplify the buying and selling of...
|
Active | FA23 (2026) |
JavaScript
React
Express.js
+5
|
Details |
|
Intelligent Fruit Ripeness Detection and Automated Counting using YOLO-based Computer Vision
In traditional agricultural and industrial practices, fruit ripeness is assessed manually ...
|
Active | FA23 (2026) |
Python
HTML/CSS
JavaScript
+11
|
Details |
|
RemitChain: A Blockchain-Based Mobile App for Low-Cost Crypto Remittance to Pakistan
Pakistan receives over $30 billion in remittances annually, yet existing platforms like We...
|
Active | FA23 (2026) |
Dart
Flutter
Firebase
+1
|
Details |
|
Fraud Prevention & Verification System for Digital Micro-Transactions
What problem does it solve?
Small-scale retailers face high risks of digital payment frau...
|
Active | FA23 (2026) |
Kotlin (Android)
Jetpack Compose
|
Details |
|
An NLP-Based system for Automated Resume Generating and Intelligent hiring
Problem Statement
In the modern job market, recruiters receive a large number of resumes,...
|
Active | FA23 (2026) |
Python
Django
|
Details |
|
COMSATS University complain management system
1. Project Scope
In-Scope:
· Online complaint submission (text, attachments, categor...
|
Active | FA23 (2026) |
Dart
Flutter
|
Details |
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Intelligent Fruit Ripeness Detection and Automated Counting using YOLO-based Computer Vision
• Agriculture experts
• Food processing industries
• Warehouse and storage managers
• Export companies
• Researchers in smart agriculture
• Detect fruits in images or real-time video using YOLO-based object detection
• Classify fruits into ripeness categories (unripe, ripe, overripe)
• Draw bounding boxes around detected fruits
• Automatically count total number of fruits in an image
• Display category-wise count (e.g., number of ripe and unripe fruits)
• Allow users to upload images through a web interface
• Provide real-time detection using camera (optional feature)
• Store and retrieve detection results for future analysis
- • YOLO-based fruit detection
- • Automated fruit counting
- • Ripeness classification using deep learning
- • Real-time and image-based detection
- • Visual output with bounding boxes and labels
- • Multi-object detection in a single image
- • Efficient and fast processing
- • Scalable for multiple fruit types
- “This system contributes to smart agriculture by integrating computer vision and deep learning to automate fruit analysis, improve efficiency, and reduce human dependency.”
The system should process images quickly with minimal delay
Real-time detection should be efficient and responsive
• Accuracy:
The model should provide high detection and classification accuracy
Results should be reliable for practical usage
• Usability:
The interface should be simple and user-friendly
Users should easily upload images and view results
• Scalability:
The system should support multiple fruit types in future
Can be extended for industrial use
• Security:
User data (if stored) should be protected
Secure handling of uploaded images