Waqar Ahmed

Academic

Career

Verbatim text

Waqar Ahmed
Cell: 923115855087 |  Email: qarimukhtar5@gmail.com
LinkedIn: https://www.linkedin.com/in/waqar-ahmed-cs?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app
Address: HOUSE NO. 129, IQABAL HOTEL ST, TENCH BHATTA , Rawalpindi , Pakistan
PROFESSIONAL PROFILE
I am a Computer Science undergrad with a deep interest in Deep Learning perticualarly in Computer Vision. Currently working on
drone imagery, NDVI generation and Deep Learning for phenotyping for my Final Year Project. I have hands-on experience with
drone image stitching, geospatial processing in QGIS and model experimentation in Pytorch. Familiar with MERN at a foundational
level with high motivation in strengthening my skills in this area. 
EDUCATION
Bachelor of Science in Computer Science
, Islamabad , 3.13 (2026)
INTERNSHIP EXPERIENCE
Cyber lab, National Aerospace Science & Technology Park (NASTP)
01-Jul-2025 - 24-Aug-2025
Integration and implementation of Face detection and recognition using Deep Learning algorithms.
FINAL YEAR PROJECT
Drone Assisted Crop Disease Detection, Phenotyping and Smart Spraying
This project focuses on crop disease detection, phenotyping, and smart spraying using RGB and NIR imagery for precision
agriculture. Due to operational constraints, initial field images were captured using mobile phones and cameras, while multi-temporal
RGB and NIR drone imagery was later provided by the course instructor. The dataset spans over one month, consisting of imagery
from 13 different dates, enabling temporal analysis of crop health. As a Computer Science student, the work primarily involves data
processing, image stitching and alignment, NDVI generation, and exploration of deep learning methods for disease detection
and phenotyping. The smart spraying hardware and execution are handled by Electrical Engineering counterparts, ensuring clear
separation between software intelligence and physical implementation. The project addresses real-world challenges such as
background noise, temporal variation, and data consistency.
TECHNICAL EXPERTISE

AI enrichment