Honeysuckle Plum Grade Classification System Based On Decision Tre

With the continuous development of rural revitalization,the fruit-growing industry has taken up a large part of the income of fruit farmers.The traditional method of fruit classification has low efficiency,a single classification,and inaccurate classification,which to a certain extent affects the economic income of fruit farmers and the sustainable development of rural industries.The traditional method of classifying the grades of Feng tang plums is subjective and inaccurate.The traditional method of classifying Feng tang plums is subjective and inefficient.This article mainly introduces a decision tree-based classification system,which can classify samples.By training a decision tree model and applying it to actual data,the system can identify the impact on samples and classify them into different levels.In the paper,the collected dataset of Feng-tang plums was first preprocessed to enhance the data and reduce the influencing factors in the data feature extraction process.Then,for the feature extraction of Feng-tang plum data,two methods,artificial method and attention mechanism,were used to extract its features and form a data table.Thirdly,using the decision tree algorithm,establish decision models for manually extracted features and attention mechanism extracted features.Finally,based on the obtained Feng-tang plum grade classification decision model,a Feng-tang plum grade classification system based on decision tree was designed using Python language in B/S mode.The experimental results show that the decision tree-based Feng-tang plum classification system designed in the paper is more accurate and improves efficiency compared to traditional classification methods.The system has significant performance advantages and application value.This decision tree-based hierarchical classification system has universality and can be applied to classification problems in various fields.At the same time,the system also has good stability and scalability,which can easily respond to changes in data volume and the needs of different application scenarios.