Abstract: Compared with the traditional image recognition methods,the depth network can extract the features with better representational ability,so as to obtain better recognition effect.In reality,most of the data provided by tasks are unlabeled or partially labeled,which makes it difficult for deep network to learn.The knowledge learned from the source domain is used for the learning of the target domain by means of transfer learning,which can alleviate this problem.In order to overcome the image-data diffe-rence between the source domain and the target domain in the transfer process,an image recognition method based on deep dyna-mic joint adaptation networks is proposed.During the training of the transfer networks,the dynamic joint adaptation method is used to realize the data distribution adaptation in the multi-layer network structure.Then the entropy minimization principle is used for the target classifier to pass through the low-density area of the target domain.At last,the image classification and recognition are realized.The experimental results show that,with this method,the average accuracy of the three transfer tasks based on 24 kinds of plant disease provided by the 2018 AI challenge competition are 97.27%,94.25% and 93.66%,which are better than other algorithms.A large number of empirical results show that the transfer learning framework based on the deep networks,meanwhile,using dynamic joint adaptation and entropy minimization principle can recognize images accurately.

Key words: Convolutional neural network, Deep learning, Domain adaption, Plant disease, Transfer learning