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 order to achieve accurate and efficient recognition of calf behavior in complex scenes such as cow overlapping, occlusion, and different light and occlusion levels, this experiment adopts the method of improving the YOLO v8 model to recognize calf behavior. A calf daily behavior dataset containing 2918 images is selected as the test benchmark through video frame extraction; a P2 small-target detection layer is introduced to improve the resolution of the input scene, which significantly improves the model recognition accuracy, and reduces the computational complexity and storage requirements of the model through the Lamp pruning method. Comparisons are made with the SSD, YOLOv5n, YOLOv8n, YOLOv8-C2f-faster-EMA, YOLO v11n, YOLO v12n, and YOLO v8-P2 advanced models. The results show that the number of parameters, floating point operations (FLOPs), model size, and mean average precision (mAP) of the model after introducing the P2 small-target detection layer and pruning with the Lamp strategy are 0.949 M, 4.0 G, 2.3 Mb, and 90.9%, respectively. The significant improvement in each index effectively reduces the model size and improves the accuracy of the network. The detection results in complex environments with different light and shading levels show that the mAP in daytime (exposure) and nighttime environments is 85.1% and 84.8%, respectively, and the average mAP in the three kinds of shading cases (light, medium, and heavy) is 87.3%, representing a lightweight, high-precision, real-time, and robust model. The results of this study provide a reference for the real-time monitoring of calf behaviors all day long in complex environments.

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.

The daily behaviors of dairy cows, including standing, drinking, eating, and lying down, are closely associated with their physical health. Efficient and accurate recognition of dairy cow behaviors is crucial for timely monitoring of their health status and enhancing the economic efficiency of farms. To address the challenges posed by complex scenarios and significant variations in target scales in dairy cow behavior recognition within group farming environments, this study proposes an enhanced recognition method based on YOLOv5. Four Shuffle Attention (SA) modules are integrated into the upsampling and downsampling processes of the YOLOv5 model’s neck network to enhance deep feature extraction of small-scale cow targets and focus on feature information, while maintaining network complexity and real-time performance. The C3 module of the model was enhanced by incorporating Deformable convolution (DCNv3), which improves the accuracy of cow behavior characteristic identification. Finally, the original detection head was replaced with a Dynamic Detection Head (DyHead) to improve the efficiency and accuracy of cow behavior detection across different scales in complex environments. An experimental dataset comprising complex backgrounds, multiple behavior categories, and multi-scale targets was constructed for comprehensive validation. The experimental results demonstrate that the improved YOLOv5 model achieved a mean Average Precision (mAP) of 97.7%, representing a 3.7% improvement over the original YOLOv5 model. Moreover, it outperformed comparison models, including YOLOv4, YOLOv3, and Faster R-CNN, in complex background scenarios, multi-scale behavior detection, and behavior type discrimination. Ablation experiments further validate the effectiveness of the SA, DCNv3, and DyHead modules. The research findings offer a valuable reference for real-time monitoring of cow behavior in complex environments throughout the day.

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%.

Traditional cow manure composting equipment often lacks ventilation, heating, and real-time monitoring and adjustment capabilities, hindering manure maturation and proper utilization of compost, moisture reduction, and optimal environmental conditions for microbial activity. To address these limitations, we optimized and redesigned the structure and control elements of conventional composting equipment. In doing so, we developed a novel cow manure composting apparatus based on the target composting quantity method. The new design features a refined ventilation and heating structure, as well as an enhanced control system using PLC and MCGS. This equipment provides improved composting effects while offering real-time monitoring of composting data, effectively promoting the composting process. Further evaluation of the modified equipment was conducted using fermented cow dung, demonstrating its feasibility. The experiment yielded positive results, indicating that the composted cow manure satisfied all necessary standards across all evaluation parameters. These parameters included the moisture content of the compost, the duration of high temperature, the pH, and the seed germination index. The seed germination rate reaches 85%, and the water content of manure is 9.3%, which is much higher than the industry standard.