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agarwalpranay02/README.md

Hi, I'm Pranay Agarwal 👋

I am a Computer Science student and AI/ML enthusiast with a strong interest in Deep Learning, Computer Vision, and real-world intelligent systems. I focus on building practical, high-impact solutions combining machine learning with real-world applications such as agriculture, edge AI, and video analytics.

Currently pursuing my B.Tech in Computer Science, I am actively working on projects involving deep learning, hybrid models, and computer vision systems, along with research in applied AI.

🔬 Research & Projects

  • 📄 TinyML Human Activity Recognition (WCAIAA 2026)

    • Lightweight deep learning model for edge devices (~92% accuracy)

  • 🌱 Hybrid Plant Disease Diagnosis (MIND 2025)

    • CNN + XGBoost hybrid system with custom dataset

  • ⚽ Soccerlytics (Ongoing Research)

    • Real-time football analytics using computer vision

🔬Patent & Datasets

• 🧾 Patent
Design Patent: Internet of Things Based Device for Real-Time Data Collection and Augmentation for Agricultural Samples
👉 View Patent

• 📊 Datasets

  • Indian Ridge Gourd Leaf Dataset (IEEE DataPort)
    Preprocessed dataset for plant disease classification (healthy vs diseased leaves)
    👉 View Dataset

  • Brinjal Leaf Disease Dataset (Mendeley Data)
    Dataset for multi-class plant disease detection
    👉 View Dataset

🧠 Skills

• Languages: Python (advanced), C++ (intermediate), Java (intermediate), JavaScript (basic), C (basic)

• AI & ML: Deep Learning, Computer Vision, CNNs, Transfer Learning, OpenCV, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost

• Core Areas: Machine Learning, Deep Learning, Computer Vision, Edge AI (TinyML), Model Optimization

• Tools & Technologies: Git, GitHub, Jupyter Notebook, Google Colab, VS Code, LaTeX

• Web & Backend: HTML, CSS, JavaScript, React (basic), Node.js (basic)

• Platforms: Windows, Linux

• Soft Skills: Problem Solving, Analytical Thinking, Team Collaboration, Adaptability

• Relevant Coursework: Data Structures & Algorithms, Object-Oriented Programming, Operating Systems, DBMS, Computer Networks

🎯 Interests

Artificial Intelligence • Machine Learning • Deep Learning • Computer Vision • Real-World AI Systems

📫 Connect with Me

Pinned Loading

  1. Hybrid-Deep-Learning-for-Plant-Disease-Diagnosis Hybrid-Deep-Learning-for-Plant-Disease-Diagnosis Public

    Hybrid deep learning-based system for plant disease diagnosis using CNNs and XGBoost. Built on a custom ridge gourd dataset with real-world images, the model achieves 92% accuracy. Includes end-to-…

    Python 1

  2. Soccerlytics-Advanced-Football-Video-Analysis-System Soccerlytics-Advanced-Football-Video-Analysis-System Public

    Soccerlytics is an comprehensive football video analysis system that tracks and analyzes matches. It generates enhanced visualizations with player tracking, ball possession statistics, team assignm…

    Python 1

  3. TinyML-Human-Activity-Recognition-on-Edge-Devices TinyML-Human-Activity-Recognition-on-Edge-Devices Public

    TinyML-based deep learning system for human activity recognition using smartphone sensor data. Implements lightweight CNN and MobileNet1D models optimized for real-time edge deployment.

    Python 1

  4. Deep_Learning_Based_Fruit_Classification Deep_Learning_Based_Fruit_Classification Public

    Deep Learning-based system for classifying fruits as fresh or rotten using CNNs and transfer learning (VGG16, MobileNetV2). Trained on a Kaggle dataset with six classes, the model achieves high acc…

    Python 1

  5. Driver-Safety-System-using-ML-and-Arduino Driver-Safety-System-using-ML-and-Arduino Public

    This Arduino-based system enhances driver safety by using sensors to monitor for drowsiness and detect alcohol on the driver's breath. It provides real-time alerts to prevent accidents caused by fa…

    Python 1

  6. Dog-Breed-Prediction-based-on-Multi-class-Classification Dog-Breed-Prediction-based-on-Multi-class-Classification Public

    This project uses deep learning and transfer learning with TensorFlow to classify dog breeds from images. Trained on the top 10 breeds from the Kaggle dataset, the model achieves over 94% validatio…

    Jupyter Notebook 1