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In this paper, a lightweight channel-wise attention model is proposed for the real-time detection of five representative pig postures: standing, lying on the belly, lying on the side, sitting, and mounting. An optimized compressed block with symmetrical structure is proposed based on model structure and parameter statistics, and the efficient channel attention modules are considered as a channel-wise mechanism to improve the model architecture.The results show that the algorithm’s average precision in detecting standing, lying on the belly, lying on the side, sitting, and mounting is 97.7%, 95.2%, 95.7%, 87.5%, and 84.1%, respectively, and the speed of inference is around 63 ms (CPU = i7, RAM = 8G) per postures image. Compared with state-of-the-art models (ResNet50, Darknet53, CSPDarknet53, MobileNetV3-Large, and MobileNetV3-Small), the proposed model has fewer model parameters and lower computation complexity. The statistical results of the postures (with continuous 24 h monitoring) show that some pigs will eat in the early morning, and the peak of the pig’s feeding appears after the input of new feed, which reflects the health of the pig herd for farmers.

Numerous modern technologies are reliant on the low-phase noise and exquisite timing stability of microwave signals. Substantial progress has been made in the field of microwave photonics, whereby low-noise microwave signals are generated by the down-conversion of ultrastable optical references using a frequency comb 1 – 3 . Such systems, however, are constructed with bulk or fibre optics and are difficult to further reduce in size and power consumption. In this work we address this challenge by leveraging advances in integrated photonics to demonstrate low-noise microwave generation via two-point optical frequency division 4 , 5 . Narrow-linewidth self-injection-locked integrated lasers 6 , 7 are stabilized to a miniature Fabry–Pérot cavity 8 , and the frequency gap between the lasers is divided with an efficient dark soliton frequency comb 9 . The stabilized output of the microcomb is photodetected to produce a microwave signal at 20 GHz with phase noise of −96 dBc Hz −1 at 100 Hz offset frequency that decreases to −135 dBc Hz −1 at 10 kHz offset—values that are unprecedented for an integrated photonic system. All photonic components can be heterogeneously integrated on a single chip, providing a significant advance for the application of photonics to high-precision navigation, communication and timing systems. We leverage advances in integrated photonics to generate low-noise microwaves with an optical frequency division architecture that can be low power and chip integrated.

Pixel-wise semantic segmentation of lactating sows and piglets is critical to explore maternal traits in smart livestock breeding and production. However, semantic segmentation algorithms do not yield satisfactory results under low-light conditions because these methods are highly dependent on image quality. Therefore, an efficient and detail-preserving low-light image enhancement is necessary and crucial for animal monitoring under low-light conditions. In this paper, a low-light enhancement method called self-calibrated acceleration and detail preserving (SADP) was proposed to improve the semantic segmentation performance of lactating sows and piglets. Specifically, a self-calibration acceleration module that accelerated the convergence among all stages was proposed to improve the computational efficiency and a semantic perceptual loss term was proposed for a high detail and semantic information preservation. Plenty of experiments demonstrated that SADP outperformed the existing well-known methods in both visual quality and efficiency (-0.013s in execution time from 0.0163s to 0.0033s, -0.0031 M in mode size from 0.0034 M to 0.0003 M), and even more improved the performance of semantic segmentation of lactating sows and piglets, raising the mean IOU from 0.8686 to 0.8872. Obviously, SADP also can be used to improve the performance of other high-level visual tasks. This may build a good foundation for the following visual tasks and further promote the livestock breeding and production.

Prediction of the greenhouse temperature and relative humidity is very important, which can forecast the environment parameters for manual intervention in advance. However, temperature and relative humidity prediction systems face two critical limitations: inconsistent temporal resolution in data acquisition and the absence of standardized protocols for environmental data collection, which collectively lead to non-uniform control strategies that compromise system interoperability in agricultural applications. This research predicted the temperature and relative humidity with different time interval in South China greenhouse by the model of BPPSO, LSSVM and RBF, which has proved their superiority in temperature and relative humidity prediction. The results showed that the R 2 of temperature and relative humidity increase gradually with the decrease of the time interval, and the time interval of 15 min got the maximum value. The R 2 of the temperature predicted by three models were 0.923, 0.923,0.912, and the R 2 of the relative humidity were 0.948,0.952, and 0.948, respectively. The prediction accuracy of relative humidity was higher than that of temperature. All three models could be used to predict temperature and relative humidity in greenhouses in South China, among which LSSVM had higher R 2 than the other two models. When the time interval was 15 min, the MAE, MAPE and RMSE of temperature were 0.574, 1.941 and 0.867, respectively, while the relative humidity of that were 2.747, 3.383 and 3.907, respectively. It concluded that the LSSVM model with time interval of 15 min was suitable to predict the temperature and relative humidity in south China greenhouse. This study provides reference for early intervention of greenhouse temperature and relative humidity management.

In modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environments. In this study, the piglet multi-behavior-recognition approach is divided into three stages. In the first stage, the LAMP pruning algorithm is used to prune and optimize redundant channels, resulting in the lightweight YOLOv8-Prune. In the second stage, based on YOLOv8, the AIFI module and the Gather–Distribute mechanism are incorporated, resulting in YOLOv8-GDA. In the third stage, using YOLOv8-GDA as the teacher model and YOLOv8-Prune as the student model, knowledge distillation is employed to further enhance detection accuracy, thus obtaining the YOLOv8-Piglet model, which strikes a balance between the detection accuracy and speed. Compared to the baseline model, YOLOv8-Piglet significantly reduces model complexity while improving detection performance, with a 6.3% increase in precision, 11.2% increase in recall, and an mAP@0.5 of 91.8%. The model was deployed on the NVIDIA Jetson Orin NX edge computing platform for the evaluation. The average inference time was reduced from 353.9 ms to 163.2 ms, resulting in a 53.8% reduction in the processing time. This study achieves a balance between model compression and recognition accuracy through the collaborative optimization of pruning and knowledge extraction.

Understanding how piglets move around sows during posture changes is crucial for their safety and healthy growth. Automated monitoring can reduce farm labor and help prevent accidents like piglet crushing. Current methods (called Joint Detection-and-Tracking-based, abbreviated as JDT-based) struggle with problems like misidentifying piglets or losing track of them due to crowding, occlusion, and shape changes. To solve this, we developed MSHMTracker, a smarter tracking system that introduces a motion-status hierarchical architecture to significantly improve tracking performance by adapting to piglets’ motion statuses. In MSHMTracker, a score- and time-driven hierarchical matching mechanism (STHM) was used to establish the spatio-temporal association by the motion status, helping maintain accurate tracking even in challenging conditions. Finally, piglet group aggregation or dispersion behaviors in response to sow posture changes were identified based on the tracked trajectory information. Tested on 100 videos (30,000+ images), our method achieved 93.8% tracking accuracy (MOTA) and 92.9% identity consistency (IDF1). It outperformed six popular tracking systems (e.g., DeepSort, FairMot). The mean accuracy of behavior recognition was 87.5%. In addition, the correlations (0.6 and 0.82) between piglet stress responses and sow posture changes were explored. This research showed that piglet movements are closely related to sow behavior, offering insights into sow–piglet relationships. This work has the potential to reduce farmers’ labor and improve the productivity of animal husbandry.

The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, lying on the belly, lying on the side, and standing. The model incorporates a Mamba Global–Local Extractor (MGLE) Module, which leverages Mamba to capture global dependencies while preserving local details through convolutional operations and channel shuffle, overcoming Mamba’s limitation in retaining fine-grained visual information. Additionally, an Adaptive Multi-Path Attention (AMPA) mechanism integrates spatial-channel attention to enhance feature focus, ensuring robust performance in complex environments and low-light conditions. To further improve detection, a Cross-Layer Feature Pyramid Transformer (CFPT) neck employs non-upsampled feature fusion, mitigating semantic gap issues where small target features are overshadowed by large target features during feature transmission. Experimental results demonstrate that MACA-Net achieves a precision of 83.1% and mAP of 85.1%, surpassing YOLOv8n by 8.9% and 4.4%, respectively. Furthermore, MACA-Net significantly reduces parameters by 48.4% and FLOPs by 39.5%. When evaluated in comparison to leading detectors such as RT-DETR, Faster R-CNN, and YOLOv11n, MACA-Net demonstrates a consistent level of both computational efficiency and accuracy. These findings provide a robust validation of the efficacy of MACA-Net for intelligent livestock management and welfare-driven breeding, offering a practical and efficient solution for modern pig farming.

A novel method is proposed based on the improved YOLOV5 and feeding functional area proposals to identify the feeding behaviors of nursery piglets in a complex light and different posture environment. The method consists of three steps: first, the corner coordinates of the feeding functional area were set up by using the shape characteristics of the trough proposals and the ratio of the corner point to the image width and height to separate the irregular feeding area; second, a transformer module model was introduced based on YOLOV5 for highly accurate head detection; and third, the feeding behavior was recognized and counted by calculating the proportion of the head in the located feeding area. The pig head dataset was constructed, including 5040 training sets with 54,670 piglet head boxes, and 1200 test sets, and 25,330 piglet head boxes. The improved model achieves a 5.8% increase in the mAP and a 4.7% increase in the F1 score compared with the YOLOV5s model. The model is also applied to analyze the feeding pattern of group-housed nursery pigs in 24 h continuous monitoring and finds that nursing pigs have different feeding rhythms for the day and night, with peak feeding periods at 7:00–9:00 and 15:00–17:00 and decreased feeding periods at 12:00–14:00 and 0:00–6:00. The model provides a solution for identifying and quantifying pig feeding behaviors and offers a data basis for adjusting the farm feeding scheme.

Induction in pineapples requires the targeted delivery of specific chemical solutions into the plant’s central core to enable batch management, a task currently reliant on manual operation. This study addressed this challenge by analyzing the physical characteristics of pineapple plants and establishing a perception-based mathematical model for core position localization. An integrated hardware–software system was developed, complemented by a human–machine interface for real-time operational monitoring. Comprehensive experiments were conducted to evaluate the spraying accuracy, nozzle response time, and prototype performance. The results demonstrate that the actuation system—comprising solenoid valves, pumps, and flowmeters—achieved an average spraying error of 2.72%. The average nozzle opening/closing time was 0.111 s; with a standard operating speed of 0.5 m/s, a delay compensation distance of 55.5 mm was implemented. In human–machine comparative trials, the automated system outperformed manual spraying in both efficiency and stability, with average errors of 7.1% and 6.4%, respectively. The system reduced chemical usage by over 67,500 mL per hectare while maintaining a miss-spray rate of 5–6%. Both two-tailed tests revealed extremely significant differences (p < 0.001). These findings confirm that the developed solution meets the operational requirements for pineapple floral induction, offering significant improvements in precision and resource efficiency.

The mechanistic underlying the favorable meat quality of Tibetan pigs has not been fully elucidated. This study integrated flavor chemistry, histomorphology, and proteomics to explore the structural and molecular features of their meat. Longissimus dorsi samples from Tibetan and Duroc pigs (n = 6 each biological replicates) were quantitatively analyzed for amino acid profiling, histological assessment, and proteomic characteristic. Statistical approaches included weighted correlation network analysis, t-tests, and functional enrichment. Tibetan pork contained 34 mg/100g more total free amino acids, notably sweet-tasting Ala (+49.2%) and Thr (+32.2%). Muscle fiber density was >250% higher and diameter > 30% smaller, indicating finer texture. Proteomics revealed 149 upregulated proteins, including 57 mitochondrial differentially expressed proteins (DEPs)—11 of which belonged to electron transport chain complexes (e.g., NDUFAB1, COX2). The significant enrichment of oxidative phosphorylation pathways may be associated with mitochondrial efficient energy metabolism under hypoxic in Tibetan pigs, potentially linking to the breed’s unique meat characteristics. Ala levels showed strong correlations with metabolic and structural protein modules. The finer fibers and mitochondrial protein profile of Tibetan pigs contribute to higher amino acid content and meat quality. This structural–metabolic–flavor axis supports both hypoxia adaptation and high meat quality. Given the central role of mitochondrial electron transport chain (ETC) proteins in energy metabolism and Ala in flavor presentation, their synergistic action provides a molecular bridge between hypoxia adaptation and meat quality. Therefore, this study suggests that ETC and Ala may serve as key biomarkers for meat quality differences, offering new perspectives for meat quality research.