Weed Recognition for Depthwise Separable Network Based on Transfer Learning

Abstract

For improving the accuracy of weed recognition under complex field conditions, a weed recognition method using depthwise separable convolutional neural network based on deep transfer learning was proposed in this study. To improve the model classification accuracy, the Xception model was refined by using model transferring and fine-tuning. Specifically, the weight parameters trained by ImageNet data set were transferred to the Xception model. Then a global average pooling layer replaced the full connection layer of the Xception model. Finally, the XGBoost classifier was added to the top layer of the model to output results. The performance of the proposed model was validated using the digital field weed images. The experimental results demonstrated that the proposed method had significant improvement in both classification accuracy and training speed in comparison of VGG16, ResNet50, and Xception depth models. The test recognition accuracy of the proposed model reached to 99.63%. Further, the training of each round time cost was 208 s, less than VGG16, ResNet50 and Xception models’, which were 248 s, 245 s and 217 s, respectively. Therefore, the proposed model has a promising ability to process image detection and output more accurate recognition results, which can be used for other crops’ precision management.