Plant leaf recognition system based on MobileNetV3
Aiming at the problem of poor model generalization in the existing plant leaf recognition research,this paper designs a plant leaf recognition system based on MobileNetV3-Large network and transfer learning.A large-scale leaf data set containing 32 plants is constructed by means of supplementary data of self-collected images and using image sharpening,flipping,and brightness enhancement.Based on the MobileNetV3-Large network and pre-training weights,the optimal hyperparameters are found to complete the transfer learning of the model,and the feature extraction and classification of 32 plant leaves are performed.Through the front-end and back-end deployment of PyQt5,this method is instantiated as a practical plant leaf recognition system.The experimental results on the test set demonstrates that MobileNetV3-Large achieves a recognition accuracy of 98.45%,which is 12.46%,1.09%and 9.62%higher than that of AlexNet,ResNet and MobileNetV2,respectively,effectively making up for the shortcomings of poor generalization of the model.The system has a good recognition effect on the leaves of 32 kinds of plants,and meets the needs of plant leaf species recognition in various scenarios.