Freelancer

Key Skills

Software

OpenCV AI Kit (OAK)

Other

Top Subject Matter

Agriculture / Crop monitoring

Safety / Computer vision (rockfall monitoring)

Environmental sustainability app (hackathon)

Top Data Types

Image

Top Task Types

Segmentation

Object Detection

Freelancer Overview

Weed Detection in Sugarcane Fields (currently developing). Core strengths include OpenCV AI Kit (OAK), OpenCV, and Other. Education includes Bachelor of Engineering, Sethu Institute of Technology (2028). AI-training focus includes data types such as Image, Computer Code, and Programming and labeling workflows including Segmentation, Object Detection, and Computer Programming.

Labeling Experience

Weed Detection in Sugarcane Fields (currently developing)

OpenCV AI Kit (OAK)ImageSegmentation

Developed an intelligent weed detection system for sugarcane fields using image processing and basic machine learning. Created or prepared visual training data to support learning weed vs. non-weed patterns in agricultural imagery. Focused on extracting usable image-based signals from field visuals for model improvement. • Worked on dataset preparation steps implied by image-processing-based detection • Framed labeling needs as weed/non-weed discrimination for training • Iterated on model-ready inputs to improve detection quality • Applied basic ML concepts to support supervision from labeled images

2025 - Present

Kadambavanam App (Hackathon Project)

Other

Built a mobile application during a Google Developer Teams Madurai hackathon for the Clean Madurai initiative. Implemented application logic that likely included collecting or organizing structured inputs for environmental sustainability features. Used software development practices to prototype and operationalize the hackathon solution. • Translated hackathon requirements into app functionality • Organized and processed user/system inputs required for app workflows • Collaborated in a team environment to deliver a working prototype • Focused on sustainability feature implementation rather than explicit AI labeling

2024 - 2024

Rockfall Detection System (Patent Work)

ImageObject Detection

Conducted independent research for a smart rockfall detection system intended for real-time safety monitoring. Designed the workflow for transforming visual signals into supervised training targets suitable for object detection. Concentrated on safety-relevant interpretation from imagery for monitoring and alerting use cases. • Prepared or specified labeled data needs for rockfall-related visual events • Supported model training via supervised labeling aligned to real-time detection • Emphasized safety application context for annotation criteria • Contributed to patent-focused system design considerations tied to detection outputs

2024 - 2024

Education

Work History