基于深度学习的植物叶片分类系统设计

Plant classification is an effective way to protect plant diversity, and it is an important basis for distinguishing plant species due to the different textures, colors, and morphological structures of plant leaves, and it is also of great significance to the study of plant cDesign of Plant Leaf Classification System Based on Deep Learninglassification. However, the traditional classification of artificial plant leaves has a low accuracy due to its strong subjectivity and large workload.In this article, we will do the following.First, this paper uses the open-source dataset on the Kaggle website, which contains five plant leaves. The dataset is divided into a training set and a test set. Three deep learning networks, AGG19, ResNet50 and AlexNet, were selected for modeling and training under the same dataset. Through comparison, Resnet50 was finally selected as the core algorithm of the system.Finally, this paper uses Qt Designer and PyUIC tools to design the system. Qt Designer designs the system interface to be easy to use and versatile. PyUIC converts the designed UI into a Python file for easy programming.