The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model’s ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management.

As the unique identifier of individual breeding pigs, the loss of ear tags can result in the loss of breeding pigs’ identity information, leading to data gaps and confusion in production and genetic breeding records, which can have catastrophic consequences for breeding efforts. Detecting the loss of ear tags in breeding pigs can be challenging in production environments due to factors such as overlapping breeding pig clusters, imbalanced pig-to-tag ratios, and relatively small-sized ear tags. This study proposes an improved method for the detection of lost ear tags in breeding pigs based on Cascade Mask R-CNN. Firstly, the model utilizes ResNeXt combined with a feature pyramid network (FPN) as the feature extractor; secondly, the classification branch incorporates the online hard example mining (OHEM) technique to improve the utilization of ear tags and low-confidence samples; finally, the regression branch employs a decay factor of Soft-NMS to reduce the overlap of redundant bounding boxes. The experiment employs a sliding window detection method to evaluate the algorithm’s performance in detecting lost ear tags in breeding pigs in a production environment. The results show that the accuracy of the detection can reach 92.86%. This improvement effectively enhances the accuracy and real-time performance of lost ear tag detection, which is highly significant for the production and breeding of breeding pigs.

UAV cloud which integrates the flexibility and re-silience of mobile cloud computing (MCC) with multiple UAV system provides drones the ability of processing compute-intensive application by offloading task to cloud. However, such task with heterogeneous quality of experience (QoE) requirement generated by massive drones becomes a troublesome burden for cloud resource allocation. Especially the endurance issue related to the energy efficiency makes the problem more complicated. This paper proposes a game theory based decentralized continuous offloading algorithm. Each drone in the UAV cloud optimizes the percentage of offloading task executed at cloud, while minimizes its overhead composed by QoE requirement and energy consumption. This algorithm can be proved to a potential game that can reach a bilateral satisfaction Nash Equilibrium (NE) by finite iteration. Numerical results under various scenario corroborate not only the effectiveness and stability of the proposed continuous offloading game, but also the superiority of computation complexity and communication overhead.

A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.

The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs.