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Simple Summary: Through the non-destructive recognition and tracking algorithm of multi-objective behaviors of beef cattle, practitioners are able to obtain beef cattle feeding information in an all-round way, which lays the foundation for intelligent farming. This study is based on the deep learning recognition algorithm YOLOv8 and tracking algorithm Deep SORT. Through optimizing the convolution module, introducing the attention mechanism, improving the re-identification network, and the trajectory generation and matching process, a new method for the non-destructive identification and tracking of multi-target behaviors of beef cattle is constructed. The average accuracy of nine behaviors can be up to 96.5%, and the accuracy of multi-target tracking is up to 92.1%, which can provide technical support for beef cattle management. The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming. [ABSTRACT FROM AUTHOR]

Simple Summary: Through beef cattle behavior recognition technology, livestock farmers can promptly identify abnormal behaviors in beef cattle, providing data and technological support for intelligent and welfare-oriented cattle farming. This study is based on computer vision's convolutional neural networks. It constructs a new method for the real-time detection of beef cattle behavior by optimizing the convolutional module and adding attention mechanisms. The method is evaluated on nine behavior datasets, including standing, lying, mounting, fighting, licking, eating, drinking, walking, and searching. It significantly improves the performance of beef cattle behavior recognition, achieving an average accuracy of 96.5%, which serves as a foundation for beef cattle health assessment and information-based farming. Behavior recognition in beef cattle is a crucial component of beef cattle behavior warning and intelligent farming. Traditional beef cattle behavior recognition faces challenges in both difficulty in identification and low accuracy. In this study, the YOLOv8n_BiF_DSC (Fusion of Dynamic Snake Convolution and BiFormer Attention) algorithm was employed for the non-intrusive recognition of beef cattle behavior. The specific steps are as follows: 45 beef cattle were observed using a fixed camera (A LINE OF DEFENSE) and a mobile phone (Huawei Mate20Pro) to collect and filter posture data, yielding usable videos ranging from 1 to 30 min in length. These videos cover nine different behaviors in various scenarios, including standing, lying, mounting, fighting, licking, eating, drinking, walking, and searching. After data augmentation, the dataset comprised 34,560 samples. The convolutional layer (CONV) was improved by introducing variable convolution and dynamic snake-like convolution modules. The dynamic snake-like convolution, which yielded the best results, expanded the model's receptive field, dynamically perceived key features of beef cattle behavior, and enhanced the algorithm's feature extraction capability. Attention mechanism modules, including SE (Squeeze-and-Excitation Networks), CBAM (Convolutional Block Attention Module), CA (Coordinate Attention), and BiFormer (Vision Transformer with Bi-Level Routing Attention), were introduced. The BiFormer attention mechanism, selected for its optimal performance, improved the algorithm's ability to capture long-distance context dependencies. The model's computational efficiency was enhanced through dynamic and query-aware perception. Experimental results indicated that YOLOv8n_BiF_DSC achieved the best results among all improved algorithms in terms of accuracy, average precision at IoU 50, and average precision at IoU 50:95. The accuracy of beef cattle behavior recognition reached 93.6%, with the average precision at IoU 50 and IoU 50:95 being 96.5% and 71.5%, respectively. This represents a 5.3%, 5.2%, and 7.1% improvement over the original YOLOv8n. Notably, the average accuracy of recognizing the lying posture of beef cattle reached 98.9%. In conclusion, the YOLOv8n_BiF_DSC algorithm demonstrates excellent performance in feature extraction and high-level data fusion, displaying high robustness and adaptability. It provides theoretical and practical support for the intelligent recognition and management of beef cattle. [ABSTRACT FROM AUTHOR]

Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets—pig faces and pig necks—which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model's target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices. [ABSTRACT FROM AUTHOR]

Cataluminescence-based sensors do not require external light sources and complex circuitry, which enables them to avoid light scattering with high sensitivity, selectivity, and widely linear range. In this study, a wireless sensor system based on hierarchical CuO microspheres assembled from nano-sheets was constructed for Volatile Organic Compound (VOC) online detection. Through sensor characteristics and data process analysis, the results showed that the luminous sensor system has good luminous characteristics, including the intensity of visible light, high signal/noise (S/N) values, and very short response and recovery times. Different VOC concentration values can be detected on multiple wavelength channels and different Cataluminescence signal spectra separations can process multiple sets of Cataluminescence data combinations concurrently. This study also briefly studied the mechanism action of the Cataluminescence sensor, which can specifically be used for VOC detecting. [ABSTRACT FROM AUTHOR]