Hongke Zhao

Facial recognition technology and related research have matured over time, but research in the field of individual animal recognition is still very limited. Therefore, this article focuses on the identification of cashmere goats with similar characteristics. First, the single shot multibox detector network was used to process the dataset. Next, transfer learning was applied to learn the characteristics of the goats, as well as the loss function is composed of Triplet Loss and Label Smoothing CrossEntropy Loss function. The result of Label Smoothing CrossEntropy Loss function is fused by multiple different branches, which is convenient for classification. We added a small number of images of 24 different breeds of sheep to each cashmere goat dataset with different ID to promote the distance between training individuals, and then used the trained model to find the number of goats with the lowest recognition accuracy. The Cycle‐Consistent Adversarial Network (Cycle‐GAN) learned the goat dataset with a high error rate in individual identification. Unlike previous studies using the Cycle‐GAN, we took the novel approach of using this network to learn and combine the features seen in photos of cashmere goats. Since the learned features were all observed in the same goats, this method achieved better results in learning the features of the goats. Finally, we found that recognition can be performed on this data with an accuracy of 93.75%. These results suggest that identification based on deep learning has a high accuracy rate, as well as great value in identifying individual cashmere goats.

Early diagnosis and accurate identification of apple tree leaf diseases (ATLDs) can control the spread of infection, to reduce the use of chemical fertilizers and pesticides, improve the yield and quality of apple, and maintain the healthy development of apple cultivars. In order to improve the detection accuracy and efficiency, an early diagnosis method for ATLDs based on deep convolutional neural network (DCNN) is proposed. We first collect the images of apple tree leaves with and without diseases from both laboratories and cultivation fields, and establish dataset containing five common ATLDs and healthy leaves. The DCNN model proposed in this paper for ATLDs recognition combines DenseNet and Xception, using global average pooling instead of fully connected layers. We extract features by the proposed convolutional neural network then use a support vector machine to classify the apple leaf diseases. Including the proposed DCNN, several DCNNs are trained for ATLDs recognition. The proposed network achieves an overall accuracy of 98.82% in identifying the ATLDs, which is higher than Inception-v3, MobileNet, VGG-16, DenseNet-201, Xception, VGG-INCEP. Moreover, the proposed model has the fastest convergence rate, and a relatively small number of parameters and high robustness compared with the mentioned models. This research indicates that the proposed deep learning model provides a better solution for ATLDs control. It could be also integrated into smart apple cultivation systems.