Abstract: To solve the low accuracy, high work intensity, high cost, and low efficiency problems in the tree species recognition, a tree species recognition system was designed and developed based on the deep learning algorism with using the machine vision software HALCON and Arduino program development technology. By building main hardware equipment of the tree species recognition system, 3 000 images of six species trees were automatically collected. All the images were randomly divided into the training set, verification set, and test set as the ratio of 7∶1.5∶1.5. Based on the HALCON target detection framework, this research compared and analyzed the training results and evaluation results of three target detection feature extraction networks: SqueezeNet, Inception-v3, and ResNet-50, respectively. The SqueezeNet network model was chosen due to its good performance to experiment the impact of tree species in different environments and growth conditions, and then a tree species recognition system was designed and constructed. The recognition experimental results showed that the average accuracy of the model was 0.735, and the recognition accuracy and recall rate on the test set was higher than 99.5%. It could accurately identify different species of trees with different upright degrees and DBH under different light conditions and the system had strong generalization performance. The on-site random test results showed that the accuracy of the network model was higher than 93%, and the system had good system stability and practicability, which can meet the requirements of actual working conditions. This research provides technical support for the intelligent management of forest resources.