Integration of Computer Vision and Machine Learning for Automated pH Prediction

In-Seong Jeon, Sukjae Joshua Kang, Chan-Woung Jeong, Seunghyeon Kim, Seong-Joo Kang

This study presents an experimental platform that integrates computer vision and machine learning to support approximate pH estimation and endpoint detection in titration experiments for science education. A Raspberry Pi-based setup was used to capture real-time solution images, which were converted into RGB data for analysis. Grid-based image preprocessing reduced artifacts caused by ripples and localized color variations. Cluster analysis identified three RGB-based solution categories that were correlated with pH. Regression analysis, including Random Forest modeling, achieved high predictive accuracy with low error. Machine learning classification models were also evaluated, with Random Forest and K-Nearest Neighbors showing strong performance for the non-linear relationship between pH and RGB values. The results support the feasibility of using BTB within its transition range for approximate pH estimation and endpoint detection in an educational setting. The system can also be used as an educational platform through which students engage with automated data collection, machine learning, and real-time analysis. By reducing subjective visual observation and improving experimental reproducibility, this approach supports the use of digital technologies in science education.