About Us
We’re BSCS 4AIS students who love bees and tech, building smart tools to help beekeepers care for their hives in a smarter way.
Our Research
iBrood is an undergraduate thesis project developed by Bachelor of Science in Computer Science (BSCS) 4A students. Our goal is to assist beekeepers in monitoring queen cell development and brood patterns using advanced AI-powered image segmentation and object detection models. We trained our YOLOv11-Medium segmentation model to achieve 95.6% precision and 98.5% mAP50 for accurate queen cell analysis.
The Researchers
Renalyn Pino
Data Annotator | Organizer | Tester
21 years old
Betina Grace Lat
Data Annotator | Organizer | Tester
23 years old
Our Mission
The goal is to provide beekeepers with smart, easy-to-use tools that use Artificial Intelligence and Computer Vision technology to assess hive health, forecast queen cell growth, and maintain healthy bee colonies.
About iBrood
iBrood is an intelligent system designed to help beekeepers monitor hive health through AI-powered analysis of queen cell development and brood patterns. Our mission is to make advanced hive monitoring accessible to beekeepers of all experience levels.