Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming.

Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

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.

The Inner Mongolia Autonomous Region is a crucial area in China with a significant advantage in animal husbandry, particularly in cattle and sheep farming. However, the disposal of the large quantities of manure produced during farming has severely impacted the industry’s healthy development. Proper treatment of the manure can convert it into organic fertilizer beneficial to farmland; otherwise, it will cause substantial environmental pollution. This study focuses on existing composting equipment and addresses the issues of cattle and sheep manure mixture ratios and compost maturity evaluation. Through experiments on the mixture of cattle and sheep manure, the optimal ratio for converting cattle and sheep manure into organic fertilizer was determined. Additionally, a fuzzy mathematical evaluation model was employed, along with experimental data, to establish a comprehensive evaluation system for aerobic compost maturity based on multiple indicators, revealing the variation patterns of maturity levels under different mixture ratios. The test results revealed that the composting equipment effectively controls the composting process, shortens the composting cycle, ensures the complete decomposition of organic matter, and meets national standards for livestock and poultry manure treatment. Regarding temperature and humidity, oxygen concentration, seed germination rate, pH value, electrical conductivity (EC), nitrogen, phosphorus, potassium content, and carbon-to-nitrogen ratio, the mixed compost of cattle and sheep manure in various ratios met the relevant standards for agricultural application. Various ratios of organic fertilizers containing cattle and sheep manure significantly promoted the growth of maize, wheat, and mung bean crops. Specifically, the compost decomposition cycle was shortest when sheep and cattle dung were mixed at a ratio of 2:1, while it was longest for all cattle dung. Finally, a fuzzy mathematical comprehensive evaluation model was established by selecting four indicators: water content, carbon-to-nitrogen ratio, apparent score, and germination index. The study demonstrates that the equipment and method offer significant advantages in efficiently treating cattle and sheep manure and producing organic fertilizer, thereby providing strong support for the sustainable development of animal husbandry.

In recent years, the global energy mix is shifting towards sustainable energy systems due to the energy crisis and the prominence of ecological climate change. Wind energy resources are abundant in cold regions, and wind turbines are increasingly operating in cold regions with wet natural environments, increasing the risk of wind turbine blade icing. To address the problem of noise source distribution and the frequency characteristic variation of wind turbines in natural icing environments, this paper uses a 112-channel microphone array to acquire the acoustic signals of a horizontal axis wind turbine with a diameter of 2.45m. Using the beamforming technique, the wind turbine noise evolution law characteristics under natural icing environment were studied by field experiments, and the noise source distribution and noise increase in different frequency bands under different icing mass and positions and different angles of attack were analyzed in detail. The results show that under the leading-edge and windward-side icing, the noise source gradually moves toward the blade tip along the spanwise direction with the increase in ice mass. In addition, the total sound pressure level at 460 r/min, 520 r/min, 580 r/min, and 640 r/min are increased by 0.82 dB, 0.85 dB, 0.91 dB, and 0.95 dB, respectively for the leading-edge icing condition in comparison with the uniform icing over the windward side of the blade.

As livestock and poultry farming expands in China, the generation of fecal waste has significantly increased. Inadequate waste management can contribute to environmental pollution. This study seeks to optimize small-scale composting systems to address the inefficiencies and the limited automation of traditional composting equipment. We redesigned the mixing blades and refined the ventilation heating system, establishing an efficient mixing mechanism and an energy-saving ventilation heating system. A control system, incorporating Monitor and Control Generated System and Programmable logic Controller, was developed for real-time monitoring and adjustment capabilities, substantially enhancing automation levels. The optimization proved effective by reducing the composting cycle from 13 days to 11.5 days, increasing the GI value from 83% to 89%, and lowering the humidity from 8.9% to 8.1%.