Research on Plant Leaf Recognition Based on BP Neural Network

Abstract

Aiming at the problems of single plant identification feature, complex algorithm and unsatisfactory recognition rate, this research proposes to use BP algorithm to establish a plant leaf identification model. This research pre-processed the image of plant leaves, and extracted 14 feature variables of the image, including colour features, shape features and texture features. Using SPSS 21.0 software to perform principal component analysis on the extracted 14 feature variables, the analysis obtained 6 the main ingredient. Using the recognition model to carry out a simulation experiment, the experimental results show that when the number of hidden layers of the model is 1, the number of hidden layer nodes is 6, the accuracy of model recognition is better, and the average accuracy of model recognition is 95.56%. From the analysis of model performance, the training accuracy and recognition accuracy of the model did not fluctuate greatly, and the graph changes relatively smoothly; when learning times is 400 times, the training and testing accuracy of the model reached more than 95%. It shows that the model has high accuracy and strong generalization ability, which provides reference for plant leaf recognition research.