This project involves conducting fatigue analysis during workouts by leveraging thermal cameras and Computer Vision AI. By monitoring and analyzing the body's thermal responses, this innovative approach allows for a deeper understanding of physical exertion, helping individuals optimize their training routines and enhance overall performance.
The development of a YOLO object detection model focused on accurately detecting and identifying chilli peppers. This project aims to create a precise and efficient solution for automating the detection of chilli peppers in various applications, such as agriculture or quality control.
This project utilizes drone cameras and Computer Vision AI for accurate aerial surveying of coconut trees. High-resolution imagery captures a comprehensive view, and our AI provides precise tree counts and health insights. Streamlining surveying, it empowers effective resource management, early issue detection, and ensures optimal coconut tree health and growth.
Enhancing quality control in manufacturing, this project focuses on Surface Defects Detection of manufactured parts through Computer Vision AI. We detect and analyze surface anomalies, ensuring a meticulous inspection process. This approach not only ensures the production of high-quality parts but also streamlines manufacturing efficiency by minimizing defects and enhancing overall product reliability.
This project focuses on Mastitis Detection in Cows through the integration of Thermal Imaging and Computer Vision AI. We aim to detect early signs of mastitis, a common udder infection in cows. This approach not only ensures the well-being of the herd but also enhances overall dairy farm productivity through proactive health management.