Accurate pig counting during herd transfers is fundamental to effective livestock management in large-scale swine production, yet existing methods struggle with bidirectional passages, boundary oscillations, and occlusion in real corridor environments. This study proposes an integrated system combining an improved YOLO-based detection model with a Hysteresis-based Multi-frame Temporal Confirmation Counting Strategy (HMTC). The YOLO11s baseline was enhanced using lightweight RepViT blocks, dynamic upsampling (DySample), and shape-aware bounding box regression (Shape-IoU). The resulting model achieves a mAP50 of 0.982 with a compact architecture of 8.28M parameters, representing a 12.3% reduction relative to the baseline while improving detection accuracy. To address bidirectional counting challenges, the HMTC strategy utilizes hysteresis-based region classification, temporal confirmation, and trajectory verification to suppress boundary jitter and ensure directional correctness. Evaluated on nine videos from a single transfer corridor, the proposed system achieves an overall counting accuracy of 99.21% on this test set and runs in real time on an embedded edge device at over 30 FPS without loss of counting accuracy. Together, the improved detection model and HMTC counting strategy provide a cohesive approach to pig passage counting, validated here under a single transfer-corridor condition; these results offer a promising basis for automated animal inventory management, pending further validation across more diverse farm environments.

Accurate multi-object tracking of group-housed pigs is essential for health monitoring in precision livestock farming, yet remains challenging due to similar pig appearances, frequent occlusions, dense interactions, and nonlinear motion. This paper proposes STA-PigTrack (Spatiotemporal Association Enhanced Pig Tracking), an appearance-free tracking method based on enhanced spatiotemporal association. The method introduces the Unscented Kalman Filter (UKF) to better handle nonlinear motion such as sudden accelerations and sharp turns, outperforming conventional Kalman-based models. To address identity loss from long-term occlusion and trajectory fragmentation, an AFLink module constructs a global cost matrix from spatiotemporal trajectory continuity, combined with the Hungarian algorithm to recover fragmented identities without relying on appearance embeddings. Evaluated on both a self-collected and a public dataset, STA-PigTrack achieved average HOTA, MOTA, and IDF1 scores of 83.82 %, 96.27 %, and 89.73 %, respectively, reducing identity switches by 70.2 % compared with the baseline SORT, while maintaining a real-time speed of 56 FPS. It outperformed DeepSORT, BotSORT, and OCSORT in identity consistency and robustness under heavy occlusion and high-density conditions. Using the obtained trajectories, group activity intensity and individual cumulative displacement were extracted as exploratory trajectory derived movement indicators, showing the feasibility of transforming tracking outputs into quantifiable behavioral features. STA-PigTrack provides a reliable real-time solution for individual tracking in complex farm environments and may provide a technical basis for future precision health monitoring and welfare related applications.

Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints, which plays a crucial role in behavior analysis and lameness detection. However, real farming scenarios, characterized by occlusions and large variations in object scale may result in poor detection results. Therefore, we introduce the atrous spatial pyramid pooling (ASPP) module into the shallow layers network of ResNet101, designed to improve the multi-scale feature extraction capability of the model. The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the parallel atrous convolutional layers to expand the model's receptive field. Furthermore, seven types of motion features, including tracking up, gait symmetry, step height balance, motion speed variability, head swing amplitude, head-neck slope and back curvature are extracted simultaneously by monitoring and tracking the motion trajectory of distinct keypoints. Several of these features represent innovative extraction models and attributes, first proposed in this study. Multiple models are trained and tested on datasets containing 2,385 frames for ablation experiments. The experiments show that, in comparison with the ResNet50, MobileNet_v2_1.0, and EfficientNet-b0 backbone networks, the training error and test error of ResNet101 are reduced by 4.04-30.12 pixels and 3.81-28.14 pixels. Therefore, ResNet101 is used as the benchmark for subsequent model improvement by adding the ASPP module. The training error and test error of the ResNet101-ASPP network are reduced by 0.27 and 0.24 pixels, respectively, compared to the benchmark network. The prediction confidence improves by 1.65-2.50% at three different dairy cow object scales. In addition, the keypoints under different occlusion conditions improve considerably, especially for small-scale keypoints, demonstrating the capability of the ASPP module for multi-scale feature extraction. By analyzing the distribution of the seven features and health, mild lameness, and severe lameness in dairy cows, it is shown that all the different features play an important role in distinguishing between different levels of lameness.

This study proposed an online early lameness detection method for dairy cow health management to overcome the inability of wearable sensor-based methods for online detection and low sensitivity to early lameness. Wearable IMU sensors collected acceleration data in stationary and moving states; a threshold discrimination module using variance of motion-direction acceleration was designed to distinguish states within 2 s, enabling rapid data screening. For moving-state windowed data, the InceptionTime network was modified with YOLOConv1D and SeparableConv1D modules plus Dropout, which significantly reduced model parameters and helped mitigate overfitting risk, enhancing generalization on the test set. Typical gait features were fused with deep features automatically learned by the network, enabling accurate discrimination among healthy, mild (early) lameness, and severe lameness. Results showed that the online detection model achieved 80.6% dairy cow health status detection accuracy with 0.8 ms single-decision latency. The recall and F1 score for lameness, including early and severe cases, reached 89.11% and 88.93%, demonstrating potential for early and progressive lameness detection. This study improves lameness detection efficiency and validates the feasibility and practical value of wearable sensor-based gait analysis for dairy cow health management, providing new approaches and technical support for monitoring and early intervention on large-scale farms.

Under large-scale farming conditions, automated sow estrus detection is crucial for improving reproductive efficiency, optimizing breeding management, and reducing labor costs. Conventional estrus detection relies heavily on human expertise, a practice that introduces subjective variability and consequently diminishes both accuracy and efficiency. Failure to identify estrus promptly and pair animals effectively lowers breeding success rates and drives up overall husbandry costs. In response to the need for the automated detection of sows’ estrus states in large-scale pig farms, this study proposes a method for detecting sows’ vulvar status and estrus based on multi-dimensional feature crossing. The method adopts a dual optimization strategy: First, the Bi-directional Feature Pyramid Network—Selective Decoding Integration (BiFPN-SDI) module performs the bidirectional, weighted fusion of the backbone’s low-level texture and high-level semantic, retaining the multi-dimensional cues most relevant to vulvar morphology and producing a scale-aligned, minimally redundant feature map. Second, by embedding a Spatially Enhanced Attention Module head (SEAM-Head) channel attention mechanism into the detection head, the model further amplifies key hyperemia-related signals, while suppressing background noise, thereby enabling cooperative and more precise bounding box localization. To adapt the model for edge computing environments, Masked Generative Distillation (MGD) knowledge distillation is introduced to compress the model while maintaining the detection speed and accuracy. Based on the bounding box of the vulvar region, the aspect ratio of the target area and the red saturation features derived from a dual-threshold method in the HSV color space are used to construct a lightweight Multilayer Perceptron (MLP) classification model for estrus state determination. The network was trained on 1400 annotated samples, which were divided into training, testing, and validation sets in an 8:1:1 ratio. On-farm evaluations in commercial pig facilities show that the proposed system attains an 85% estrus detection success rate. Following lightweight optimization, inference latency fell from 24.29 ms to 18.87 ms, and the model footprint was compressed from 32.38 MB to 3.96 MB in the same machine, while maintaining a mean Average Precision (mAP) of 0.941; the accuracy penalty from model compression was kept below 1%. Moreover, the model demonstrates robust performance under complex lighting and occlusion conditions, enabling real-time processing from vulvar localization to estrus detection, and providing an efficient and reliable technical solution for automated estrus monitoring in large-scale pig farms.

With the development of precision livestock farming, in order to achieve the goal of fine management and improve the health and welfare of dairy cows, research on dairy cow motion monitoring has become particularly important. In this study, considering the problems surrounding a large amount of model parameters, the poor accuracy of multi-target tracking, and the nonlinear motion of dairy cows in dairy farming scenes, a lightweight detection model based on improved YOLO v11n was proposed and four tracking algorithms were compared. Firstly, the Ghost module was used to replace the standard convolutions in the YOLO v11n network and a more lightweight attention mechanism called ELA was replaced, which reduced the number of model parameters by 18.59%. Then, a loss function called SDIoU was used to solve the influence of different cow target sizes. With the above improvements, the improved model achieved an increase of 2.0 percentage points and 2.3 percentage points in mAP@75 and mAP@50-95, respectively. Secondly, the performance of four tracking algorithms, including ByteTrack, BoT-SORT, OC-SORT, and BoostTrack, was systematically compared. The results show that 97.02% MOTA and 89.81% HOTA could be achieved when combined with the OC-SORT tracking algorithm. Considering the demand of equipment in lightweight models, the improved object detection model in this paper reduces the number of model parameters while offering better performance. The OC-SORT tracking algorithm enables the tracking and localization of cows through video surveillance alone, creating the necessary conditions for the continuous monitoring of cows.

Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-Autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-Autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learningbased gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-ofview limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

In order to analyze the application and development potential of pose estimation in animal behavior recognition and animal welfare research, this paper took the deep learning-based pose estimation method as a breakthrough, summarized the research progress and directions of animal pose estimation from the perspective of two-dimensional and three-dimensional space, and introduced the common data sets and evaluation indexes. Then integrated the research results related to animal behavior recognition based on pose estimation, focused on the algorithms of keypoint detection and behavior classification and their features. Pose estimation provides skeleton information and motion features for tasks such as action recognition and behavior analysis, and became a non-contact monitoring method for animal behavior recognition and abnormal information warning. However, due to the limitation of small training data set, the research and development of animal pose estimation was relatively slow compared to human pose estimation. Therefore, the used of cross-domain learning to further improve its performance has become an emerging tool in recent years. This review expands research ideas and research methods for researchers related to intelligent animal behavior recognition, animal welfare research, and smart farming.

Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making.

针对奶牛行为判别自动化水平不足、准确率低的问题,采用惯性测量单元(IMU)和卷积神经网络(CNN),对细粒度奶牛行为判别进行研究.结果表明:1)在KNN、SVM、BPNN、CNN和LSTM 5个模型中,CNN模型在奶牛行为分类测试集上的准确率最高.2)含有三轴加速度计、陀螺仪和磁力计的IMU更加适用于奶牛行为分类,其分类效果优于含一种传感器的IMU.3)传感器频率与分类模型的性能相关,频率越高,正确率越高,当传感器频率设置为25 Hz时,奶牛行为判别效果最好.4)在1、2和4 s这3种时间窗中,使用4 s时间窗的奶牛行为分类模型性能最好.5)采用最优配置时,卷积神经网络模型能够有效的判别奶牛站立、躺卧2种状态,正确率为99％;可以对奶牛卷食、咀嚼、站立反刍、躺卧反刍、躺卧休息、站立休息6类行为进行判别,正确率为85％.采用IMU和卷积神经网络算法,可以有效的对细粒度奶牛行为进行判别,为奶牛养殖的自动化、智能化管理提供支撑.

1 2021年马铃薯市场形势回顾 回顾2021年,我国马铃薯市场形势具有薯价同比先低后高、总体价格水平偏低、贸易顺差同比大幅增加等3个显著特点.

12022 年上半年市场形势回顾 2022 年上半年我国马铃薯市场行情特征可概括为薯价前期同比偏低,后期同比偏高和总体价格水平较常年偏低等3 个特点. 1.1 薯价前期同比偏低 2022 年第1 季度马铃薯市场供应以2021 年产季库存薯为主.总体来看,2022 年上半年前期,马铃薯市场供需关系偏松,价格较2021 年同期呈下跌走势(图1).据农业农村部监测,2022 年第1 季度马铃薯批发均价为每千克2.35 元,同比下跌7.1％.

The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features.

Livestock pollution is one of the main sources of agricultural pollution, which has a negative impact on the global environment. Monitoring, simulation and early warning of major pollutants emitted from livestock production is of great significance for reducing agricultural pollution. Especially, real-time comprehensive monitoring and early warning of the concentration and distribution of harmful gases could improve the harm of livestock production to people, livestock itself and the environment, and increase the safety level of livestock production. This study focused on the perception and monitoring of the discharge status of livestock farming simulation technics, mainly to carry out Internet of Things-based monitoring of the main components of livestock culture pollutants, and to use odor gas air dispersion software “ModOdor” to simulate the spread of pollutants. This study was aimed to determine the characteristics of pollutant diffusion in typical farms, which could provide decision reference to odor hygienic buffer zone and minimum shelter distance to achieve the ecological and safety objectives of livestock farming.

1 2020年我国马铃薯市场形势回顾 回顾2020年,我国马铃薯市场行情表现为前期价格高企、后期价格同比下跌、价格总体高位运行以及贸易顺差同比减少等4个特点. 1.1 前期价格高企 马铃薯市场供应主体在2020年1—4月为2019年产季马铃薯,在5—7月为2020年产季春 薯.据农业农村部监测数据,2020年1—4月马铃薯批发均价为3.10元 · kg-1,比2019年同期上涨25.8％;5—7月的批发均价为2.65元 · kg-1,比2019年同期上涨4.5％.

随着食品安全与保障体系的不断发展,微观层面的食物保障成为食物保障的新要求.零售环节是城市食物供给的终端, “菜篮子”产品零售终端的空间格局直接影响着家庭、个人对食物的可获性.以GIS空间分析技术为支撑,对北京市四环内“菜篮子”产品零售终端、道路网络与居民点进行空间统计和分析,基于居民点至最临近零售终端的道路距离探析居民的“菜篮子”产品可获性.最后,根据研究结果提出改善“菜篮子”产品可获性、改进微观层面城市居民食物保障的对策建议.

1 2020年上半年市场形势回顾 回顾 2020 年上半年,我国马铃薯市场行情延续 2019 年向好趋势,表现为月度价格总体同比明显提高、均价创近 10 年来新高以及贸易顺差同比大幅增加等方面.

马铃薯具有耐旱、耐寒、耐瘠薄的特点,适应范围广,增产空间大,种植区域几乎覆盖我国所有省份,是我国重要的粮食作物、经济作物和饲料作物.马铃薯产业在助力脱贫攻坚、促进农业高质量发展和实施乡村振兴战略等方面发挥着重要作用.2017～2018年我国马铃薯市场行情总体低迷,那么2019年我国马铃薯市场运行呈现出哪些特征?2020年市场走势如何?产业发展还存在哪些制约因素?本文对此进行了研究.

Lameness in dairy cattle could cause significant economic losses to the dairy industry. Detection of lameness in a timely manner is critical to the high-quality development of dairy industry. The traditional method is visual locomotion scoring by dairy farmers, which is low efficiency, high cost and subjective. The demand for automated lameness detection is increasing. The review was conducted to find out the current state and challenges of automatic lameness detection technology development and to learn from the latest findings. The current automatic lameness detection systems were reviewed in this paper mainly rely on five technologies or combinations thereof, including machine vision, pressure distribution measuring system, wearable sensor system, behavior analysis and classification; the principle, function and features of these technologies were analyzed. Machine vision technique is to extract feature variables (e.g. back arch, head bob, abduction, stride length, walking speed, temperature, etc.) from video recordings of cattle movement by image processing. Pressure distribution measuring system contains an array of load cells to sense gait variables, when dairy cattle are walking by. By using accelerometer with high frequency data collection, the gait cycle parameters can be extracted and used for lameness detection. By using wearable devices, the number of lying/standing bouts and their duration, the total time spent lying, standing and ruminating per day can be recorded for individual cattle. The lameness can also be detected by behavior analysis. Currently, most of these studies were in the stage of sensor development or validation of algorithm. A few studies were in the stage of validation of performance and decision support with early warning system. The challenges to apply automatic lameness detection system in dairy farm includes the difficulties of acquiring high quality data of lameness features, lack of techniques to detect early lameness, identification errors caused by individual gait differences among dairy cattle, difficulties to function well in unstructured environment and difficulties to evaluate the benefits. To accelerate the development of automatic lameness detection systems, recommendations are proposed as follows: ①promoting lameness data sharing and data exchange among dairy farms; ②developing individual-based lameness classification model; ③developing multifunctional smart station which can detect lameness, measure body condition score, weighing, etc; ④evaluating the significance of automatic lameness detection to the dairy industry from the perspective of animal welfare, environment and food safety.

Through exploring the current research status of veterinary drug circulation and use, the principal issues in the process of veterinary drug circulation and use are discussed. This paper summarizes the application of GSP-based traceability and correlation technology in veterinary drug circulation, package identification aggregation and splitting conversion technology in veterinary drug logistics, real-time perception technology in cold chain transportation of veterinary biological products, and "one-to-one" matching technology, veterinary drugs, livestock and poultry in intelligent perception of veterinary drug circulation and use process.