Winter Research Intern (WRIS 2024) — AI/ML (plant image disease detection)
ImageConducted exploratory data analysis and statistical analysis on a large plant image dataset to assess data imbalance and key visual patterns for model readiness. Built and evaluated a proof-of-concept deep learning pipeline aimed at AI-driven disease detection in precision agriculture before production deployment. Applied multiple ML/DL approaches to improve disease classification performance by a reported margin. • EDA and statistical analysis on 5,000+ plant images using visualizations • Identified dataset imbalance and salient image patterns for training considerations • Trained/evaluated CNN-based and attention-based models for classification • Used few-shot learning methods to enhance plant disease classification accuracy
2024 - 2025