Here we list all volunteers that have helped us. Consider this as a hall of fame
Engineering
| Person name |
Focus |
| Aleksei Prokopov |
Worked on digital scales IoT device (hardware) |
| Vjatลกeslav Kekลกin |
Published on our whitepaper (research) |
| Kurban Ramazanov |
Helped with UI/UX, designing prototypes to imporove web-app experience |
| Alonso Solis |
participated in andmetorm hackathon, worked on UI design and FE components |
| Adrian Ala |
participated in andmetorm hackathon, worked on UX flow and pitching |
| Ahmed Daoudi |
Helped with telemetry-api, building initial service version in express/nodejs |
| Muhammad Zain Shakeel |
Helped with 3D model of the robotic beehive |
| Natalia Kinash |
Participated in the hackathon where she helped Reinis Indans with satellite imagery processing |
| Reinis Indans |
Participated in a hackathon where he did most of the heavy work on data collection and model training for satellite pollination sub-project - satellite-pollination-map microservice |
Machine Learning Research - University of Tartu
Students from University of Tartu Machine Learning course (MTAT.03.227), taught by Dmytro Fishman, Associate Professor, who contributed production-ready AI models for bee and hornet detection
| Person name |
Focus |
Impact |
| Kreete Kuusk |
Bee type detection (T30) |
YOLOv8 model training, manual dataset review and quality assurance, team coordination |
| Danni Zhang |
Bee type detection (T30) |
YOLOv10 model training, data preprocessing with image tiling, copy-paste augmentation for class balancing |
| Jasper Luik |
Bee type detection (T30) |
Dataset search on Roboflow, RT-DETR model exploration, initial YOLOv10 training on balanced/unbalanced data |
| Rasmus Mirma |
Bee type detection (T30) |
YOLOv12 model training, dataset preparation from Mississippi State University, model visualization on images and videos |
| Albert Unn |
Hornet detection (T14) |
Model development, created YOLOv8-nano achieving 96.7% precision for hornet detection at hive entrance |
| Kadi-Liis Kivi |
Hornet detection (T14) |
Data labeling and preprocessing for hornet detection dataset (~1250 images) |
| Karen Roht |
Hornet detection (T14) |
Model architecture research and training, comparative analysis of detection models |
| Otto Kase |
Hornet detection (T14) |
Dataset creation and augmentation, specialized in data pipeline development |
| Norman Tolmats |
Multi-class bee detection (T41) |
Auto-annotation pipeline, generated 30k+ labeled images, led YOLO-based detection experiments |
| Mihkel Kulu |
Multi-class bee detection (T41) |
Dataset evaluation, model iteration and validation for real-time bee classification |
| Joonas Tiitson |
Multi-class bee detection (T41) |
Built initial YOLO pipeline using Roboflow dataset, evaluated alternative architectures like RT-DETR |
| Markus Kivime |
Multi-class bee detection (T41) |
Manual annotation of validation dataset, quality assurance for model evaluation |
Testing
Product Ideas
**Thank you all!
