AI/ML Researcher & Computer Vision Engineer. Building intelligent systems that bridge the gap between research and real-world deployment — from deep learning models to edge devices.
Get in Touch →| Degree | B.Tech Computer Science & Engineering |
| Institute | Birla Institute of Technology, Mesra, India |
| Semester | 8th Semester (Final Year) |
| CGPA | 8.33 / 10 |
| Location | Jaipur, Rajasthan, India |
| pranayagarwal2511@gmail.com |
I am Pranay Agarwal, a final-year B.Tech Computer Science & Engineering student at Birla Institute of Technology (BIT), Mesra, Ranchi, with a CGPA of 8.33. I specialize in deep learning, computer vision, and edge AI — with a strong track record of translating research into real-world impact.
As an undergraduate, I have published datasets on IEEE DataPort and Mendeley Data, hold a granted design patent from the Government of India, presented at international conferences, and had my work recognized by Springer Nature and featured in regional media.
I am passionate about building intelligent systems that bridge the gap between research and real-world deployment — from training deep learning models to deploying them on edge devices and extracting actionable insights from complex data.
8th Semester · Final Year · AI/ML & Computer Vision
Physics · Chemistry · Mathematics
Working on applied AI/ML research including computer vision systems, deep learning model development, and agricultural AI applications. Collaborating on research publications, patent development, and real-world deployment of intelligent systems.
Contributed to software engineering projects involving data pipelines, backend development, and system design. Gained hands-on experience in professional software development workflows and collaborative engineering practices.
Hybrid CNN + XGBoost system for automated plant disease diagnosis on Indian ridge gourd. Custom dataset collected from real farms. ~92% accuracy — presented at MIND-2025, MNIT Jaipur.
View on GitHub →Real-time football match analysis with YOLO-based multi-object tracking, ball possession intelligence, team assignment, and 2D tactical reconstruction from raw video footage.
View on GitHub →Lightweight deep learning model for HAR deployed on resource-constrained edge devices. Presented at WCAIAA 2026, National Forensic Sciences University, Goa.
View on GitHub →CNN and transfer learning (VGG16, MobileNetV2) to classify fruits as fresh or rotten across 6 classes. End-to-end pipeline from data preprocessing to model evaluation.
View on GitHub →Arduino-based embedded safety system detecting driver drowsiness and alcohol presence in real time. Provides immediate hardware alerts to prevent road accidents.
View on GitHub →TensorFlow transfer learning model classifying top 10 dog breeds from images. Achieves 94%+ validation accuracy on the Kaggle dataset with fine-tuned feature extraction.
View on GitHub →Responsive tourism website with an interactive chatbot, travel packages showcase, and contact form backed by PHP + MySQL for data storage and async submission.
View on GitHub →Functional electronic voting machine on Arduino Uno with push-button voting interface, accurate vote counting for multiple candidates, and hardware result display.
View on GitHub →9,000 preprocessed images across 3 classes with 15 preprocessing techniques applied per image. Collected from real agricultural fields in India.
Authors: Pranay Agarwal (1st), Param Sharma, Vikas Sharma
DOI: 10.21227/469z-vm04
View on IEEE DataPort →Preprocessed dataset of brinjal leaves for automated plant disease detection. Associated with a Data in Brief paper currently under review at Elsevier Inc.
Role: Second Author · Data in Brief paper under review
View on Mendeley Data →Recognised by Springer Nature (in collaboration with ICSSR & Ministry of Education, Govt. of India) for contributions to the India Research Tour 2025 Contest, advancing the research ecosystem through innovation and excellence. Springer Nature's official LinkedIn account publicly featured the submission.
Research on AI-based plant disease detection for ridge gourd was featured in Punjab Kesari — a leading regional newspaper. The article covered the patent, IEEE dataset, and Fasal Saathi mobile app, highlighting how BIT Mesra researchers are empowering farmers to detect crop disease via mobile phone.
The ridge gourd leaf image dataset was specially featured in the official IEEE DataPort August 2025 Newsletter — recognizing its contribution to open agricultural AI research and its utility for the global research community.
Presented the research paper "TinyML-Based Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices" at the 7th World Conference on Artificial Intelligence: Advances and Applications (WCAIAA 2026), organized by SCRS at NFSU Goa.
Volunteered in support of prosthetic limb distribution and fitting for individuals with disabilities. Assisted beneficiaries during rehabilitation and medical camps, facilitated coordination between medical staff and patients, and contributed to improving mobility and quality of life for underprivileged patients.
Served as a volunteer caretaker assisting elderly residents with daily needs and emotional support over an 8-month engagement. Built strong interpersonal skills, empathy, and a deep sense of social responsibility through sustained community service.
Served as Senior Coordinator for the Sports Committee at BIT Mesra, Sports Fest 2023. Responsible for organizing and managing sports events during the annual fest.
BIT Mesra · Sports Fest 2023Achieved 2nd place in the Football Tournament at BIT Mesra Sports Fest 2023, demonstrating teamwork, athletic ability, and competitive spirit.
🥈 2nd Place · Sports Fest 2023Actively contributed as a Coordinator in the Cultural Committee at BIT Mesra, helping organize and manage cultural events and student activities on campus.
BIT Mesra · Cultural CommitteeI'm open to research collaborations, internship opportunities, and interesting conversations at the intersection of AI, computer vision, and real-world systems. Feel free to reach out through any of the channels below.