In the process of collecting facial images of cattle in the field, some features of the collected images end up going missing due to the changeable posture of the cattle, which makes the recognition accuracy decrease or impossible to recognize. This paper verifies the practical effects of the classical matching algorithms ORB, SURF, and SIFT in bull face matching recognition. The experimental results show that the traditional matching algorithms perform poorly in terms of matching accuracy and matching time. In this paper, a new matching recognition model is constructed. The model inputs the target cattle facial data from different angles into the feature extraction channel, combined with GMS (grid-based motion statistics) algorithm and random sampling consistent algorithm, to achieve accurate recognition of individual cattle, and the recognition process is simple and fast. The recognition accuracy of the model was 85.56% for the Holstein cow face dataset, 82.58% for the Simmental beef cattle, and 80.73% for the mixed Holstein and Simmental beef cattle dataset. The recognition model constructed in the study can achieve individual recognition of cattle in complex environments, has good robustness to matching data, and can effectively reduce the effects of data angle changes and partial features missing in cattle facial recognition.

A microwave photonics instantaneous frequency measurement scheme with 14 channels based on an optical frequency comb (OFC) is proposed. In this scheme, a 14-line flat OFC is generated by cascading a dual-parallel Mach–Zehnder modulator (DPMZM) with a Mach–Zehnder modulator (MZM). The intercepted microwave signal with multiple-frequency components can be measured by using DPMZM, Fabry–Perot filter (FPF), wavelength division multiplexer (WDM), and optical power detector array. This scheme can measure and analyze the frequency of microwave signals in the ranges of 0.5–13.5 GHz, 13.5–26.5 GHz, and 26.5–39.5 GHz with the measurement accuracy of ± 0.5 G H z . The reconfigurability of the system can be realized by adjusting the comb-line spacing of the OFC and the free spectral range (FSR) of the FPF.

The pose of cows reflects their body condition, and the information contained in the skeleton can provide data support for lameness, estrus, milk yield, and contraction behavior detection. This paper presents an algorithm for automatically detecting the condition of cows in a real farm environment based on skeleton spatio-temporal features. The cow skeleton is obtained by matching Partial Confidence Maps (PCMs) and Partial Affinity Fields (PAFs). The effectiveness of skeleton extraction was validated by testing 780 images for three different poses (standing, walking, and lying). The results indicate that the Average Precision of Keypoints (APK) for the pelvis is highest in the standing and lying poses, achieving 89.52% and 90.13%, respectively. For walking, the highest APK for the legs was 88.52%, while the back APK was the lowest across all poses. To estimate the pose, a Multi-Scale Temporal Convolutional Network (MS-TCN) was constructed, and comparative experiments were conducted to compare different attention mechanisms and activation functions. Among the tested models, the CMS-TCN with Coord Attention and Gaussian Error Linear Unit (GELU) activation functions achieved precision, recall, and F1 scores of 94.71%, 86.99%, and 90.69%, respectively. This method demonstrates a relatively high detection rate, making it a valuable reference for animal pose estimation in precision livestock farming.

Toxic acetone gas emissions and leakage are a potential threat to the environment and human health. Gas sensors founded on metal oxide semiconductors (MOS) have become an effective strategy for toxic gas detection with their mature process. In the present work, an efficient acetone gas sensor based on Au-modified ZnO porous nanofoam (Au/ZnO) is synthesized by polyvinylpyrrolidone-blowing followed by a calcination method. XRD and XPS spectra were utilized to investigate its structure, while SEM and TEM characterized its morphology. The gas sensitivity of the Au/ZnO sensors was investigated in a static test system. The results reveal that the gas-sensitive performance of porous ZnO toward the acetone can be enhanced by adjusting the loading ratio of noble Au nanoparticles. Specifically, the Au/ZnO sensor prepared by the Au loading ratio of 3.0% (Au/ZnO-3.0%) achieved a 100 ppm acetone gas response of 20.02 at the optimum working temperature of 275 °C. Additionally, a portable electronic device used a STM32 primary control chip to integrate the Au/ZnO-3.0% gas sensor with other modules to achieve the function of detecting and alarming toxic acetone gas. This work is of great significance for efficiently detecting and reducing acetone emissions.

Accurate and non-contact measurement of cattle body dimensions is essential for precision livestock management. This study presents YOLOv8-DMC, a lightweight deep learning model optimized for anatomical keypoint detection in side-view images of cattle. The model integrates three attention modules—DRAMiTransformer, MHSA-C2f, and CASimAM—to improve robustness under occlusion and lighting variability. Following keypoint prediction, a 16-neighborhood depth completion and pass-through filtering process are applied to generate clean, colored point clouds. This enables precise 3D localization of keypoints by matching them to valid depth values. The model achieves $A{P}_{@0.5}$ of 0.931 and AP@[0.50:0.95] of 0.868 on a dataset of over 7000 images, improving baseline accuracy by 2.14% and 3.09%, respectively, with only 0.35 M additional parameters and 0.9 GFLOPs in complexity. For real-world validation, strictly lateral-view RGB-D images from 137 cattle were collected, with ground-truth manual measurements. Compared with manual measurements, the average relative errors are 2.43% for body height, 2.26% for hip height, 3.65% for body length, and 4.48% for cannon circumference. The system supports deployment on edge devices, providing an efficient and accurate solution for 3D cattle measurement in real-world farming conditions.

Accurate and efficient access to Mongolian horse body size information is an important component in the modernization of the equine industry. Aiming at the shortcomings of manual measurement methods, such as low efficiency and high risk, this study converts the traditional horse body measure measurement problem into a measurement keypoint localization problem and proposes a top-down automatic Mongolian horse body measure measurement method by integrating the target detection algorithm and keypoint detection algorithm. Firstly, the SimAM parameter-free attention mechanism is added to the YOLOv8n backbone network to constitute the SimAM–YOLOv8n algorithm, which provides the base image for the subsequent accurate keypoint detection; secondly, the coordinate regression-based RTMPose keypoint detection algorithm is used for model training to realize the keypoint localization of the Mongolian horse. Lastly, the cosine annealing method was employed to dynamically adjust the learning rate throughout the entire training process, and subsequently conduct body measurements based on the information of each keypoint. The experimental results show that the average accuracy of the SimAM–YOLOv8n algorithm proposed in this study was 90.1%, and the average accuracy of the RTMPose algorithm was 91.4%. Compared with the manual measurements, the shoulder height, chest depth, body height, body length, croup height, angle of shoulder and angle of croup had mean relative errors (MRE) of 3.86%, 4.72%, 3.98%, 2.74%, 2.89%, 4.59% and 5.28%, respectively. The method proposed in this study can provide technical support to realize accurate and efficient Mongolian horse measurements.