A noncontact pH level sensing indicator using computer vision and knowledge-based systems

• Main Authors: Luta, Raphael Benedict G., Ong, Anthony Christopher L., Lao, Selwyn Jenson C., Baldovino, Renann G., Bugtai, Nilo T., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Computer vision; Expert systems (Computer science); Hydrogen-ion concentration—Measurement > Automation; Manufacturing; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2932
• Institution: De La Salle University

A computer vision-based approach in identifying the pH level of a substance requires the use of multiple computer or machine vision techniques which include image processing, and object/contour detection, counting, or tracking. This paper proposes a pH level indicator system with knowledge-based systems (KBS) which has the capability of detecting, tracking, and identifying the level of a pH level indicator image. Moreover, the data that the system utilizes derives from KBS instead of being explicitly programmed. Furthermore, the study has a graphical user interface which allows the operator to easily use the system. The user may also add data to the knowledge-base, which means that the system improves over time. Compared with the traditional pH monitoring setup, the results in this study show that a computer vision-based approach is viable in determining the pH level of a substance. © 2017 IEEE.