Research on Automatic Classification Model of Landscaping Based On Particle Swarm Optimization Algorithm

With the gradual popularization of landscaping and floriculture, the importance of accurately and efficiently classifying flowers has become increasingly prominent. Focusing on this need, this study collected a large number of image data of flowers, extracted features including color, texture and shape, explored the key factors of flower classification, and identified different varieties of flowers. In order to further improve the performance of the classification model, we use particle swarm optimization to optimize the feature selection process, screen out the most influential features for classification, and build a more accurate classifier. The experimental results of this study show that the classification model optimized by PSO is significantly superior to the traditional classification methods in terms of accuracy and recall rate, which not only effectively improves the efficiency of flower classification, but also improves the accuracy of classification, and fully meets the market demand for the specialization of the flower products.