Toward Robot-Assisted Classification and Selective Picking of V. harveyi and Soil Bacteria via Motility Analysis of Inverted Microscope Videos Using XGBoost
Abstract
This study proposes a method for accurately estimating the mixing ratio of V. harveyi and soil bacteria by analyzing motility in inverted microscope videos and extracting 24 features. Using an XGBoost model, the proposed method outperformed SVM and 1D-CNN approaches. The proposed analytical method will be implemented into a MD-based screening system integrating an inverted microscope, automated stage, and robotic micromanipulator to enable real-time automated classification, selection, and retrieval of antagonistic bacteria during microscopic observation.
Fields of Study & Subjects
Cell Image Analysis Techniques
Micro and Nano Robotics
Smart Agriculture and AI
Publication details
- Journal / source
- IEEE/SICE International Symposium on System Integration (SII)
- Pages
- 800–805
- Publication date
- 11 January 2026
- Language
- EN