HSV Color Space Based Automated Chemical Titrator

In order to solve the problems of long manual titration time, cumbersome operation, reliance on human eye judgment, and errors in titration endpoint in chemical quantitative testing. This paper designs a automatic chemical solution titration equipment and proposes a titration recognition method based on HSV color space. A typical chemical titration color model is established, and the RGB color space is transformed into HSV color space for titration area recognition and titration endpoint recognition. At the same time, an electrical control system with a microcontroller as the core, including a Micro infusion Syringe Pump, a mobile screw, a stirring device, and a visual light source, as well as an upper computer monitoring software for recognition algorithm implementation and human-machine interaction, is designed. Taking the determination of soil organic matter content as an example, the performance test was carried out, and the test results show that the titration error of the device is less than 0.3 ml, and it is more efficient than the manual titration, with good recognition rate and control accuracy. It not only solves the problem of human resources and the intervention of the human factor, but also overcomes the defects of inaccurate discrimination that occurs in the traditional use of the RGB color space as the basis for judging the end point of the titration.

Authors

Institutions

Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/iai63275.2024.10729948
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

HSV Color Space Based Automated Chemical Titrator

Bingjie Zhang, Meng Li, Juan Zhang, Qing Song et al.
IoT-based Smart Home Systems
article

HSV Color Space Based Automated Chemical Titrator

Bingjie Zhang, Meng Li, Juan Zhang, Qing Song, Xu Lujian
article en

Abstract

In order to solve the problems of long manual titration time, cumbersome operation, reliance on human eye judgment, and errors in titration endpoint in chemical quantitative testing. This paper designs a automatic chemical solution titration equipment and proposes a titration recognition method based on HSV color space. A typical chemical titration color model is established, and the RGB color space is transformed into HSV color space for titration area recognition and titration endpoint recognition. At the same time, an electrical control system with a microcontroller as the core, including a Micro infusion Syringe Pump, a mobile screw, a stirring device, and a visual light source, as well as an upper computer monitoring software for recognition algorithm implementation and human-machine interaction, is designed. Taking the determination of soil organic matter content as an example, the performance test was carried out, and the test results show that the titration error of the device is less than 0.3 ml, and it is more efficient than the manual titration, with good recognition rate and control accuracy. It not only solves the problem of human resources and the intervention of the human factor, but also overcomes the defects of inaccurate discrimination that occurs in the traditional use of the RGB color space as the basis for judging the end point of the titration.

University of Jinan (CN), Shandong Institute for Product Quality Inspection (CN)
Affordable and clean energy
Openalex Percentile: Top 83%
IoT-based Smart Home Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

HSV Color Space Based Automated Chemical Titrator — Bingjie Zhang, Meng Li, et al. · (2024) | TGRS Research Map | TGRS