IntroductionThe purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed.MethodsThe paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features.ResultsUsing the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models.DiscussionThe experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots.

Flexible strain sensor has attracted much attention because of its potential application in human motion detection. In this work, the prepared strain sensor was obtained by encapsulating electrospun carbonized sponge (CS) with room temperature vulcanized silicone rubber (RTVS). In this paper, the formation mechanism of conductive sponge was studied. Based on the combination of carbonized sponge and RTVS, the strain sensing mechanism and piezoresistive properties are discussed. After research and testing, the CS/RTVS flexible strain sensor has excellent fast response speed and stability, and the maximum strain coefficient of the sensor is 136.27. In this study, the self-developed CS/RTVS sensor was used to monitor the movements of the wrist joint, arm elbow joint and fingers in real time. Research experiments show that CS/RTVS flexible strain sensor has good application prospects in the field of human motion monitoring.

The scale of deer breeding has gradually increased in recent years and better information management is necessary, which requires the identification of individual deer. In this paper, a deer face dataset is produced using face images obtained from different angles, and an improved residual neural network (ResNet)-based recognition model is proposed to extract the features of deer faces, which have high similarity. The model is based on ResNet-50, which reduces the depth of the model, and the network depth is only 29 layers; the model connects Squeeze-and-Excitation (SE) modules at each of the four layers where the channel changes to improve the quality of features by compressing the feature information extracted through the entire layer. A maximum pooling layer is used in the ResBlock shortcut connection to reduce the information loss caused by messages passing through the ResBlock. The Rectified Linear Unit (ReLU) activation function in the network is replaced by the Exponential Linear Unit (ELU) activation function to reduce information loss during forward propagation of the network. The preprocessed 6864 sika deer face dataset was used to train the recognition model based on SE-Resnet, which is demonstrated to identify individuals accurately. By setting up comparative experiments under different structures, the model reduces the amount of parameters, ensures the accuracy of the model, and improves the calculation speed of the model. Using the improved method in this paper to compare with the classical model and facial recognition models of different animals, the results show that the recognition effect of this research method is the best, with an average recognition accuracy of 97.48%. The sika deer face recognition model proposed in this study is effective. The results contribute to the practical application of animal facial recognition technology in the breeding of sika deer and other animals with few distinct facial features.

With the gradual increase of the scale of the breeding industry in recent years, the intelligence level of livestock breeding is also improving. Intelligent breeding is of great significance to the identification of livestock individuals. In this paper, the cattle face images are obtained from different angles to generate the cow face dataset, and a cow face recognition model based on SK_ResNet is proposed. Based on ResNet-50 and using a different number of sk_Bottleneck, this model integrates multiple receptive fields of information to extract facial features at multiple scales. The shortcut connection part connects to the maximum pooling layer to reduce information loss; the ELU activation function is used to reduce the vanishing gradient, prevent overfitting, accelerate the convergence speed, and improve the generalization ability of the model. The constructed bovine face dataset was used to train the SK-ResNet-based bovine face recognition model, and the accuracy rate was 98.42%. The method was tested on the public dataset and the self-built dataset. The accuracy rate of the model was 98.57% on the self-built pig face dataset and the public sheep face dataset. The accuracy rate was 97.02%. The experimental results verify the superiority of this method in practical application, which is helpful for the practical application of animal facial recognition technology in livestock.