The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R², RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.

The weight of live pigs is directly related to their health, nutrition management, disease prevention and control, and the overall economic benefits to livestock enterprises. Direct weighing can induce stress responses in pigs, leading to decreased productivity. Therefore, modern livestock industries are increasingly turning to non-contact techniques for estimating pig weight, such as automated monitoring systems based on computer vision. These technologies provide continuous, real-time weight-monitoring data without disrupting the pigs’ normal activities or causing stress, thereby enhancing breeding efficiency and management levels. Two methods of pig weight estimation based on image and point cloud data are comprehensively analyzed in this paper. We first analyze the advantages and disadvantages of the two methods and then discuss the main problems and challenges in the field of pig weight estimation technology. Finally, we predict the key research areas and development directions in the future.

Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species.

Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model’s performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the cow head region and the mouth region. The ResNet-50-FPN network is employed to extract the cow mouth features, and the CBAM attention mechanism is incorporated to further improve the algorithm’s detection accuracy. Subsequently, the object detection results are combined with optical flow information to eliminate false detections. Finally, an interpolation approach is adopted to design a frame complementary algorithm that corrects the detection frame of the cow mouth region. This interpolation algorithm is employed to rectify the detection frame of the cow’s mouth region, addressing the issue of missed detections and enhancing the accuracy of ruminant mouth region detection. To overcome the challenges associated with the inaccurate extraction of small-scale optical flow information and interference between different optical flow information in multi-objective scenes, an enhanced GMFlowNet-based method for multi-objective cow ruminant optical flow analysis is proposed. To mitigate interference from other head movements, the MeanShift clustering method is utilized to compute the velocity magnitude values of each pixel in the vertical direction within the intercepted ruminant mouth region. Furthermore, the mean square difference is calculated, incorporating the concept of range interquartile, to eliminate outliers in the optical flow curve. Finally, a final filter is applied to fit the optical flow curve of the multi-object cow mouth movement, and it is able to identify rumination behavior and calculate chewing times. The efficacy, robustness, and accuracy of the proposed method are evaluated through experiments, with nine videos capturing multi-object cow chewing behavior in different settings. The experimental findings demonstrate that the enhanced Faster R-CNN algorithm achieved an 84.70% accuracy in detecting the ruminant mouth region, representing an improvement of 11.80 percentage points over the results obtained using the Faster R-CNN detection approach. Additionally, the enhanced GMFlowNet algorithm accurately identifies the ruminant behavior of all multi-objective cows, with a 97.30% accuracy in calculating the number of ruminant chewing instances, surpassing the accuracy of the FlowNet2.0 algorithm by 3.97 percentage points. This study provides technical support for intelligent monitoring and analysis of rumination behavior of dairy cows in group breeding.

Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the “smart campus,” many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students’ associations. In this paper, we propose a visual analytic method to construct students’ association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges, such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char–word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model’s decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts.