1. Developed a YOLOv5 Deep Learning segmentation and classification model on a dataset of 500+ images to detect and classify leaves as healthy or diseased in a plant disease detection system.2. Enhanced image segmentation by cleaning 300+ raw coastal images using CVAT and trained a YOLOv5 model for coastal hazard identification, leveraging AWS EC2 for model training.3. Designed and implemented a Flask-based UI to simulate the plant disease detection system and evaluate its efficiency using real-world images.

1. Developed the frontend using Angular Dart and backend with GoLang and MongoDB, enabling user deletion and filtering in the admin UI to improve user management efficiency.2. Implemented a sort feature on the user list webpage using Bootstrap, allowing sorting by the last update date.3. Built a GoLang backend for an education website to manage user account details within a MongoDB database.