No abstract

Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation of the model using common linear operations without reducing the feature map. In addition, the convolutional block attention module (CBAM) is introduced after each stage of the model to help the model to give different weights to each part of the input and extract the more critical and important information. In our experiments, a total of 13 cows’ side view images were collected to train the model, and the final recognition accuracy of the model was 98.58%, which was 4.8 percentage points better than the recognition accuracy of the original ResNet50, the number of model parameters was reduced by 24.85 times, and the model size was only 3.61 MB. In addition, to verify the validity of the model, it is compared with other networks and the results show that our model has good robustness. This research overcomes the shortcomings of traditional recognition methods that require human extraction of features, and provides theoretical references for further animal recognition.

To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on improved deep residuals convolutional neural network (Im-ResNet). We built an image acquisition system to obtain the freshness classification dataset of lettuce leaves. For improving the classification accuracy, we developed an image acquisition system for curating the freshness of lettuce leaves. Then, we proposed a novel method that was derived from the existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): the new method factored extra convolutional layer, pooling layer, fullyconnected layers, and a random ReLU (RReLU) activation function. We also performed the corresponding experiments using the Im-ResNet network compared with four network architectures (AlexNet, GoogleNet, VGG16 and ResNet50). The experimental results showed that the proposed network had more significant advantages in the recognition accuracy and loss value of lettuce freshness compared with the traditional deep networks. The recognition accuracy of the validation set of the proposed model can reach to 95.60%. Different from the physical and chemical methods, our scheme can automatically and non-destructively classify the freshness of lettuce.