Search Results (9)

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

In pig farming, multi-object tracking (MOT) algorithms are effective tools for identifying individual pigs and monitoring their health, which enhances management efficiency and intelligence. However, due to the considerable variation in breeding environments across different pig farms, existing models often struggle to perform well in unfamiliar settings. To enhance the model’s generalization in diverse tracking scenarios, we have innovatively proposed the SDGTrack method. This method improves tracking performance across various farming environments by enhancing the model’s adaptability to different domains and integrating an optimized tracking strategy, significantly increasing the generalization of group pig tracking technology across different scenarios. To comprehensively evaluate the potential of the SDGTrack method, we constructed a multi-scenario dataset that includes both public and private data, spanning ten distinct pig farming environments. We only used a portion of the daytime scenes as the training set, while the remaining daytime and nighttime scenes were used as the validation set for evaluation. The experimental results demonstrate that SDGTrack achieved a MOTA score of 80.9%, an IDSW of 24, and an IDF1 score of 85.1% across various scenarios. Compared to the original CSTrack method, SDGTrack improved the MOTA and IDF1 scores by 16.7% and 33.3%, respectively, while significantly reducing the number of ID switches by 94.6%. These findings indicate that SDGTrack offers robust tracking capabilities in previously unseen farming environments, providing a strong technical foundation for monitoring pigs in different settings. Full article

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. Full article

In this study, the implementation of traditional machine learning models in the intelligent management of swine is explored, focusing on the impact of LDA preprocessing on pig facial recognition using an SVM. Through experimental analysis, the kernel functions for two testing protocols, one utilizing an SVM exclusively and the other employing a combination of LDA and an SVM, were identified as polynomial and RBF, both with coefficients of 0.03. Individual identification tests conducted on 10 pigs demonstrated that the enhanced protocol improved identification accuracy from 83.66% to 86.30%. Additionally, the training and testing durations were reduced to 0.7% and 0.3% of the original times, respectively. These findings suggest that LDA preprocessing significantly enhances the efficiency of individual pig identification using an SVM, providing empirical evidence for the deployment of SVM classifiers in mobile and embedded systems. Full article

In this study, we propose an intelligent drinking water controller based on programmable logic controller (PLC) specifically designed for pig breeding, which significantly reduces the water waste caused by the use of traditional drinking bowls by regulating the frequency and flow of water release. In addition, the drinking water system has a tracking and recording function, which can record the frequency and duration with which fattening pigs drink water in each pen in detail, thus providing farmers with a wealth of pig health and behavior data to help optimize breeding management decisions. In order to deeply analyze the effects of the intelligent drinking water controller on the growth, resources environment and economic benefits of fattening pigs under the condition of large-scale breeding, a single factor comparison experiment was designed.In this experiment, 84 fattening pigs were selected and distributed in 12 pens. Among them, six pens were randomly designated as the control group;the pig in this group used ordinary drinking water bowls for the water supply. The other six pens were designated as the experimental group;the pigs in this group used the intelligent drinking water controller. The experimental results showed that in the experimental group with the intelligent drinking water controller, the average daily water waste per finishing pig was only 0.186 L (p < 0.05), accounting for only 25.98% of the average daily water waste per pig in the control group (p < 0.05). In terms of water quality, the intelligent drinking water controller also showed better performance, and the performance indicators were effectively reduced, with the highest reduction reaching 39.86%, which greatly reduced water pollution. Compared with the traditional drinking bowl, the average daily weight increment of fattening pigs in the pen using the intelligent drinking water controller was increased by 0.02 kg. In terms of long-term benefits, the PLC-based intelligent drinking water controller significantly improves the economic returns of the farm and has a positive impact on pig health. The high frequency data collection of the pigs’ drinking habits through the intelligent drinking water controller can also provide data support for the subsequent establishment of a pig water-drinking behavior analysis model. Full article

The behavior of pigs is intricately tied to their health status, highlighting the critical importance of accurately recognizing pig behavior, particularly abnormal behavior, for effective health monitoring and management. This study addresses the challenge of accommodating frequent non-rigid deformations in pig behavior using deformable convolutional networks (DCN) to extract more comprehensive features by incorporating offsets during training. To overcome the inherent limitations of traditional DCN offset weight calculations, the study introduces the multi-path coordinate attention (MPCA) mechanism to enhance the optimization of the DCN offset weight calculation within the designed DCN-MPCA module, further integrated into the cross-scale cross-feature (C2f) module of the backbone network. This optimized C2f-DM module significantly enhances feature extraction capabilities. Additionally, a gather-and-distribute (GD) mechanism is employed in the neck to improve non-adjacent layer feature fusion in the YOLOv8 network. Consequently, the novel DM-GD-YOLO model proposed in this study is evaluated on a self-built dataset comprising 11,999 images obtained from an online monitoring platform focusing on pigs aged between 70 and 150 days. The results show that DM-GD-YOLO can simultaneously recognize four common behaviors and three abnormal behaviors, achieving a precision of 88.2%, recall of 92.2%, and mean average precision (mAP) of 95.3% with 6.0MB Parameters and 10.0G FLOPs. Overall, the model outperforms popular models such as Faster R-CNN, EfficientDet, YOLOv7, and YOLOv8 in monitoring pens with about 30 pigs, providing technical support for the intelligent management and welfare-focused breeding of pigs while advancing the transformation and modernization of the pig industry. Full article

The utilization of ear tags for identifying breeding pigs is a widely used technique in the field of animal production. Ear tag dropout can lead to the loss of pig identity information, resulting in missing data and ambiguity in production management and genetic breeding data. Therefore, the identification of ear tag dropout is crucial for intelligent breeding in pig farms. In the production environment, promptly detecting breeding pigs with missing ear tags is challenging due to clustering overlap, small tag targets, and uneven sample distributions. This study proposes a method for detecting the dropout of breeding pigs’ ear tags in a complex environment by integrating an attention mechanism. Firstly, the approach involves designing a lightweight feature extraction module called IRDSC using depthwise separable convolution and an inverted residual structure; secondly, the SENet channel attention mechanism is integrated for enhancing deep semantic features; and finally, the IRDSC and SENet modules are incorporated into the backbone network of Cascade Mask R-CNN and the loss function is optimized with Focal Loss. The proposed algorithm, Cascade-TagLossDetector, achieves an accuracy of 90.02% in detecting ear tag dropout in breeding pigs, with a detection speed of 25.33 frames per second (fps), representing a 2.95% improvement in accuracy, and a 3.69 fps increase in speed compared to the previous method. The model size is reduced to 443.03 MB, a decrease of 72.90 MB, which enables real-time and accurate dropout detection while minimizing the storage requirements and providing technical support for the intelligent breeding of pigs. Full article

To explore the application of a traditional machine learning model in the intelligent management of pigs, in this paper, the influence of PCA pre-treatment on pig face identification with RF is studied. By this testing method, the parameters of two testing schemes, one adopting RF alone and the other adopting RF + PCA, were determined to be 65 and 70, respectively. With individual identification tests carried out on 10 pigs, accuracy, recall, and f1-score were increased by 2.66, 2.76, and 2.81 percentage points, respectively. Except for the slight increase in training time, the test time was reduced to 75% of the old scheme, and the efficiency of the optimized scheme was greatly improved. It indicates that PCA pre-treatment positively improved the efficiency of individual pig identification with RF. Furthermore, it provides experimental support for the mobile terminals and the embedded application of RF classifiers. Full article

Accurate identification and intelligent counting of pig herds can effectively improve the level of fine management of pig farms. A semantic segmentation and counting network was proposed in this study to improve the segmentation accuracy and counting efficiency of pigs in complex image segmentation. In this study, we built our own datasets of pigs under different scenarios, and set three levels of number detection difficulty—namely, lightweight, middleweight, and heavyweight. First, an image segmentation model of a small sample of pigs was established based on the DeepLab V3+ deep learning method to reduce the training cost and obtain initial features. Second, a lightweight attention mechanism was introduced, and attention modules based on rows and columns can accelerate the efficiency of feature calculation and reduce the problem of excessive parameters and feature redundancy caused by network depth. Third, a recursive cascade method was used to optimize the fusion of high- and low-frequency features for mining potential semantic information. Finally, the improved model was integrated to build a graphical platform for the accurate counting of pigs. Compared with FCNNs, U-Net, SegNet, and DenseNet methods, the DeepLab V3+ experimental results show that the values of the comprehensive evaluation indices P, R, AP, F₁-score, and MIoU of LA-DeepLab V3+ (single tag) are higher than those of other semantic segmentation models, at 86.04%, 75.06%, 78.67%, 0.8, and 76.31%, respectively. The P, AP, and MIoU values of LA-DeepLab V3+ (multiple tags) are also higher than those of other models, at 88.36%, 76.75%, and 74.62%, respectively. The segmentation accuracy of pig images with simple backgrounds reaches 99%. The pressure test of the counting network can calculate the number of pigs with a maximum of 50, which meets the requirements of free-range breeding in standard piggeries. The model has strong generalization ability in pig herd detection under different scenarios, which can serve as a reference for intelligent pig farm management and animal life research. Full article