Accurate identification of sheep is important for achieving precise animal management and welfare farming in large farms. In this study, a sheep face detection method based on YOLOv3 model pruning is proposed, abbreviated as YOLOv3-P in the text. The method is used to identify sheep in pastures, reduce stress and achieve welfare farming. Specifically, in this study, we chose to collect Sunit sheep face images from a certain pasture in Xilin Gol League Sunit Right Banner, Inner Mongolia, and used YOLOv3, YOLOv4, Faster R-CNN, SSD and other classical target recognition algorithms to train and compare the recognition results, respectively. Ultimately, the choice was made to optimize YOLOv3. The mAP was increased from 95.3% to 96.4% by clustering the anchor frames in YOLOv3 using the sheep face dataset. The mAP of the compressed model was also increased from 96.4% to 97.2%. The model size was also reduced to 1/4 times the size of the original model. In addition, we restructured the original dataset and performed a 10-fold cross-validation experiment with a value of 96.84% for mAP. The results show that clustering the anchor boxes and compressing the model using this dataset is an effective method for identifying sheep. The method is characterized by low memory requirement, high-recognition accuracy and fast recognition speed, which can accurately identify sheep and has important applications in precision animal management and welfare farming.

With the rapid development of power grid construction, The Power grid is becoming more and more expansion; the reliability of the equipment operation is the important precondition for the safe and stable operation of power grid. The insulator is an important component of the primary equipment of the power grid. So it is important to research high efficient, accurate equipment to detector the insulativity of insulator. This paper is based on current problems of insulator detection, An online insulator detection device based on intelligent robot is studied. By installing the equipment onto the robot, the robot will be able to detect online. There is no need for artificially high screening tests to reduce the worker's strength. The DC ultrahigh-voltage transmission line is the latest transmission. The research on these insulators is scant. Through the experiment, device for detecting the insulator resistance was developed. Through experiment, proved the feasibility of this approach.

With the problem of charged insulator zero detection in UHV transmission line has become increasingly prominent, In this paper, We take the UHV transmission line as the background to design and develop a kind of insulator detection robot based on STM32, which can realize the automatic detection of insulator chain, this paper mainly introduces the electrical system design and mechanical design of insulator detection robot. The insulator detection robot greatly improves the efficiency of insulator detection and automation of power operation and maintenance, reduces the cost of manpower operation and creates higher economic and social benefits.

Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.

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