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.

Potato is the fourth largest food crop in the world. Disease is an important factor restricting potato yield. Disease detection based on deep learning has strong advantages in network structure, training speed, detection accuracy, and other aspects. This article took potato leaf diseases (early blight and viral disease) as the research objects, collected disease images to construct a disease dataset, and expanded the dataset through data augmentation methods to improve the quantity and diversity of the dataset. Four classic deep learning networks (VGG16, MobilenetV1, Resnet50, and Vit) were used to train the dataset, and the VGG16 network had the highest accuracy of 97.26%; VGG16 was chosen as the basic research network. A new, improved algorithm, VGG16S, was proposed to solve the problem of large network parameters by using three improvement methods: changing the network structure of the VGG16 network from “convolutional layer + flattening layer + fully connected layer” to “convolutional layer + global average pooling”, integrating CBAM attention mechanism, and introducing Leaky ReLU activation function for learning and training. The improved VGG16S network has a parameter size of 15 M (1/10 of VGG16), and the recognition accuracy of the test set is 97.87%. This article used response surface analysis to optimize hyperparameters, and the test results indicated that VGG16S, after hyperparameter tuning, had further improved its diagnostic performance. At last, this article completed ablation experiments and public dataset testing. The research results will provide a theoretical basis for the timely adoption of corresponding prevention and control measures, improving the yield and quality of potatoes and increasing economic benefits.

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 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.

Greenhouses, as important parts of facility agriculture, can reduce the restrictions on agricultural production imposed by the natural environment and make rational and efficient use of production resources. We conducted long-term, continuous testing of temperature, humidity, light intensity, and CO₂ concentration parameters in a heliostat greenhouse in the central and western parts of the Inner Mongolia Autonomous Region, a cold and arid region of northern China. A large amount of data was processed by statistical observation, simulation analysis, and 3D reconstruction to obtain the overall distribution, variation pattern, and mathematical model of the regional greenhouse environment in time and space. The results show that the temperature, humidity, light intensity, and CO₂ concentration in the greenhouse have significant daily variation patterns, that there are strong coupling relationships between light intensity–CO₂ concentration–time and indoor temperature–light intensity–CO₂ concentration, that the coefficients of determination (R²) of the mathematical models are 0.88 and 0.89, and that the standard errors (RMSE) are 49.67 ppm and 45.30 ppm, respectively. The environmental parameters were fitted with high accuracy in order to provide scientific data for the cultivation of heliostats in the region.

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.