Plant leaves can be used to effectively detect plant diseases. However, the number of images of unhealthy leaves collected from various plants is usually unbalanced. It is difficult to detect diseases using such an unbalanced dataset. We used DoubleGAN (a double generative adversarial network) to generate images of unhealthy plant leaves to balance such datasets. We proposed using DoubleGAN to generate high-resolution images of unhealthy leaves using fewer samples. DoubleGAN is divided into two stages. In stage 1, we used healthy leaves and unhealthy leaves as inputs. First, the healthy leaf images were used as inputs for the WGAN (Wasserstein generative adversarial network) to obtain the pretrained model. Then, unhealthy leaves were used for the pretrained model to generate 64*64 pixel images of unhealthy leaves. In stage 2, a superresolution generative adversarial network (SRGAN) was used to obtain corresponding 256*256 pixel images to expand the unbalanced dataset. Finally, compared with images generated by DCGAN (Deep convolution generative adversarial network). The dataset expanded with DoubleGAN, the generated images are clearer than DCGAN, and the accuracy of plant species and disease recognition reached 99.80% and 99.53%, respectively. The recognition results are better than those from the original dataset.

For trees, leaves are often used for identification, but the shape of leaves changes greatly, bark will be another identifying feature. However, it is difficult to recognize by a single organ when there are intra class differences and inter class similarities between leaves or bark. So we fuse features of leaf and bark. Firstly, we collected 17 species of leaves and bark of trees through field shooting and web crawling. Then propose a method of combining convolution neural network (CNN) with cascade fusion, additive fusion algorithm, bilinear fusion and score level fusion. Finally, the features extracted from the leaves and bark are fused in the ReLu layer and Fully connected layer. The method was compared with single organ recognition, Support Vector Machines (SVM), and existing fusion methods, results show that the two organ fusion method proposed are better than the other recognition methods, and recognition accuracy is 87.86%. For similar trees, when it is impossible to accurately determine its species by a single organ, the fusion of two organs can effectively improve this situation.