Automatic titration detection method of organic matter content based on machine vision

This article proposes an automatic titration algorithm for organic matter content detection based on machine vision, which addresses the disadvantages of high risk factor, strong odor, significant pollution to laboratory environment, and slow efficiency of manual titration in organic matter detection. Firstly, by analyzing the color change characteristics during the titration process, machine learning techniques are used to classify the titration speed, and a titration experiment state recognition model is constructed to divide the titration speed into four categories and improve titration efficiency; Secondly, through a large number of titration experiments to collect relevant data and extract key feature parameters, an efficient titration algorithm based on histogram similarity was designed to accurately identify titration endpoints and improve detection accuracy. This study not only solves the limitations of manual operation in traditional titration methods, but also provides new ide..., , , # Automatic titration detection method of organic matter content based on machine vision

### Project Overview

This project aims to optimize the titration speed through machine learning models and establish an automatic titration algorithm by setting up an HSV model to accurately determine the endpoint. We used three different machine learning models: Decision Tree, K- K-nearest neighbor ( KNN ) , and support vector machine ( SVM ), and compared their performance. The specific instructions for how to reproduce are included in the README in each model folder.

### Target

-Train and evaluate the performance of decision tree, KNN and SVM models on the titration state prediction task.

Compare the accuracy, precision, recall, and F1 score of these models.

-Select the best model to use for the HSV model in the automatic titration algorithm .

### method

1. Data preparation :

* Collect and preprocess titration experiment data.
* Split the dataset into training and testing sets.
* In this...,