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Open AccessArticle

PDA-YOLO: An Early Detection Method for Egg Fertilization Rate Based on Position-Decoupled Attention

by Yifan Zhou, Zhengxiang Shi, Geqi Yan, Haiqing Peng, Fuwei Li, Wei Liu and Dapeng Li

Agriculture 2026, 16(7), 784; https://doi.org/10.3390/agriculture16070784 - 2 Apr 2026

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Abstract

This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an [...] Read more.

This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an enhanced YOLOv8-based object detection model featuring a position-decoupled attention strategy. Specifically, a lightweight C2f-SE module is integrated into the backbone to amplify subtle feature responses in low-contrast regions, while a CBAM is deployed prior to the detection head to mitigate background clutter through precise spatial attention. Experimental results on a self-constructed Hailan White egg dataset show that at the critical 60 h incubation stage, PDA-YOLO achieves a Recall of 91.5% and an mAP@0.5 of 97.4%, outperforming the YOLOv8 baseline while maintaining a real-time inference speed of 62.1 FPS. Grad-CAM visualizations confirm the model’s ability to focus on vascular textures and suppress noise. Furthermore, the model demonstrates robust performance under varying illumination (180–540 lumens), effectively mitigating missed detections in low light and recognition degradation from overexposure. This work provides a scalable, real-time solution for non-destructive, early-stage detection of poultry health and fertilization status in commercial hatcheries. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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25 pages, 2891 KB

Open AccessArticle

Automated Measurement of Sheep Body Dimensions via Fusion of YOLOv12n-Seg-SSM and 3D Point Clouds

by Xiaona Zhao, Xifeng Liu, Zihao Gao, Xinran Liang, Yanjun Yuan, Yangfan Bai, Zhimin Zhang, Fuzhong Li and Wuping Zhang

Agriculture 2026, 16(2), 272; https://doi.org/10.3390/agriculture16020272 - 21 Jan 2026

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Abstract

Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic [...] Read more.

Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic measurement of body height, body length, and chest circumference from side-view images of sheep. The model employs a synergistic strategy that combines semantic segmentation with 3D point cloud geometric fitting. It incorporates the SegLinearSimAM feature enhancement module, the SEAttention channel optimization module, and the ENMPDIoU loss function to improve measurement robustness under complex backgrounds and occlusions. After segmentation, valid RGB-D point clouds are generated through depth completion and point cloud filtering, enabling 3D computation of key body measurements. Experimental results demonstrate that the improved model outperforms the baseline YOLOv12n-Seg: the mAP@0.5 for segmentation reaches 94.20%, the mAP@0.5 for detection reaches 95.00% (improvements of 0.5 and 1.3 percentage points, respectively), and the recall increases to 99.00%. In validation tests on 43 Hu sheep, the R² values for chest circumference, body height, and body length were 0.925, 0.888 and 0.819, respectively, with measurement errors within 5%. The model requires only 10.71 MB of memory and 9.9 GFLOPs of computation, enabling real-time operation on edge devices. This study demonstrates that the proposed method achieves non-contact automatic measurement of sheep body dimensions, providing a practical solution for on-site growth monitoring and intelligent management in livestock farms. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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25 pages, 7956 KB

Open AccessArticle

A Lightweight Facial Landmark Recognition Model for Individual Sheep Based on SAMS-KLA-YOLO11

by Yangfan Bai, Xiaona Zhao, Xinran Liang, Zhimin Zhang, Yuqiao Yan, Fuzhong Li and Wuping Zhang

Agriculture 2026, 16(2), 151; https://doi.org/10.3390/agriculture16020151 - 7 Jan 2026

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Abstract

Accurate and non-contact identification of individual sheep is important for intelligent livestock management, but remains challenging due to subtle inter-individual differences, breed-dependent facial morphology, and complex farm environments. This study proposes a lightweight sheep face detection and keypoint recognition framework based on an [...] Read more.

Accurate and non-contact identification of individual sheep is important for intelligent livestock management, but remains challenging due to subtle inter-individual differences, breed-dependent facial morphology, and complex farm environments. This study proposes a lightweight sheep face detection and keypoint recognition framework based on an improved YOLO11 architecture, termed SAMS-KLA-YOLO11. The model incorporates a Sheep Adaptive Multi-Scale Convolution (SAMSConv) module to enhance feature extraction across breed-dependent facial scales, a Keypoint-Aware Lightweight Attention (KLAttention) mechanism to emphasize biologically discriminative facial landmarks, and the Efficient IoU (EIoU) loss to stabilize bounding box regression. A dataset of 3860 images from 68 individuals belonging to three breeds (Hu, Dorper, and Dorper × Hu crossbreeds) was collected under unconstrained farm conditions and annotated with five facial keypoints. On this dataset, the proposed model achieves higher precision, recall, and mAP than several mainstream YOLO-based baselines, while reducing FLOPs and parameter count compared with the original YOLO11. Additional ablation experiments confirm that each proposed module provides complementary benefits, and OKS-based evaluation shows accurate facial keypoint localization. All results are obtained on a single, site-specific dataset without external validation or on-device deployment benchmarks, so the findings should be viewed as an initial step toward practical sheep face recognition rather than definitive evidence of large-scale deployment readiness. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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24 pages, 10093 KB

Open AccessArticle

An Improved YOLOv8n-Based Method for Multi-Object Individual Cattle Recognition Using Facial Features in Feeding Passages

by Wenju Zhang, Wensheng Wang, Yaowu Wang, Saydigul Samat and Xinwen Chen

Agriculture 2025, 15(24), 2536; https://doi.org/10.3390/agriculture15242536 - 7 Dec 2025

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Abstract

Accurate recognition of each cattle in group environments is essential for modern precision livestock management. This study proposed a multi-object cattle recognition method based on deep learning, enabling precise recognition in feeding passages. A dataset comprising facial images from 135 cattle was constructed, [...] Read more.

Accurate recognition of each cattle in group environments is essential for modern precision livestock management. This study proposed a multi-object cattle recognition method based on deep learning, enabling precise recognition in feeding passages. A dataset comprising facial images from 135 cattle was constructed, and a data augmentation strategy tailored to cattle facial characteristics was designed to enhance model generalisation. The YOLOv8n network was selected from a comparative experiment and further optimised. For multi-object bounding box regression, the standard CIoU loss was replaced by the MPDIoU loss, improving the mAP50 by 5.4% through optimised corner distance computation. In addition, a coordinate attention mechanism was embedded within the C2F module to strengthen the model’s spatial perception of key facial regions such as the eyes and nose, resulting in a 5.8% improvement in recognition precision. A comparative experiment between image-level segmentation and cattle-level segmentation datasets was carried out, and the proposed method was further validated on an untrained external test set collected from actual feeding Passages. The results demonstrate that, even under challenging conditions such as occlusion and illumination variation, the improved model achieved a classification accuracy of 88% while maintaining an average inference speed of 96.9 frames per second. This non-invasive, real-time recognition approach provides a novel solution for precision feeding in group-housed environments and offers valuable insights for improving the efficiency of livestock monitoring and feeding management systems. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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22 pages, 30314 KB

Open AccessArticle

Knowledge-Enhanced Deep Learning for Identity-Preserved Multi-Camera Cattle Tracking

by Shujie Han, Alvaro Fuentes, Jiaqi Liu, Zihan Du, Jongbin Park, Jucheng Yang, Yongchae Jeong, Sook Yoon and Dong Sun Park

Agriculture 2025, 15(18), 1970; https://doi.org/10.3390/agriculture15181970 - 18 Sep 2025

Cited by 1 | Viewed by 1468

Abstract

Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to [...] Read more.

Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to ensure identity preservation across long video sequences. A large-scale dataset was collected from five synchronized 4K cameras in a commercial barn, capturing both full-body movements and frontal facial views. The system employs center point detection and BEV projection for cross-view trajectory association, while periodic face recognition during feeding refreshes identity assignments and corrects errors. Evaluations on a two-day dataset of more than 600,000 images demonstrate robust performance, with an AssPr of 84.481% and a LocA score of 78.836%. The framework outperforms baseline trajectory matching methods, maintaining identity consistency under dense crowding and noisy labels. These results demonstrate a practical and scalable solution for automated cattle monitoring, advancing data-driven livestock management and welfare. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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18 pages, 1907 KB

Open AccessArticle

Detection of Neonatal Calf Diarrhea Using Suckle Pressure and Machine Learning

by Beibei Xu, Claira R. Seely, Tapomayukh Bhattacharjee and Taika von Konigslow

Agriculture 2025, 15(17), 1831; https://doi.org/10.3390/agriculture15171831 - 28 Aug 2025

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Abstract

Neonatal calf diarrhea (NCD) remains one of the most prevalent and economically burdensome health challenges in preweaned calves, leading to compromised growth, increased morbidity, and high mortality rates worldwide. While traditional methods such as physical examination and clinical health scoring are widely used, [...] Read more.

Neonatal calf diarrhea (NCD) remains one of the most prevalent and economically burdensome health challenges in preweaned calves, leading to compromised growth, increased morbidity, and high mortality rates worldwide. While traditional methods such as physical examination and clinical health scoring are widely used, they often require trained personnel, are resource-intensive, and are prone to subjectivity, which limits their scalability in large dairy operations. This observational cohort study investigated the feasibility of using suckle pressure measurement combined with machine learning (ML) techniques for NCD detection. A total of 51 female Holstein calves on a commercial dairy farm were enrolled at birth and health scored daily from 1 to 21 days of age. Suckle pressures were measured at 1, 3, 5, 7, 10, 14, and 21 days, as well as daily following NCD diagnosis until fecal consistency returned to normal. Pressure measurements were captured using impression film-wrapped nipples, producing 349 images, of which 54 were from calves diagnosed with NCD. Image features, including pixel density, color saturation, entropy, and histogram-based features, were extracted for analysis. Multiple ML classifiers—Support Vector Machine, K-Nearest Neighbors, Random Forest, Gradient Boosting, and Easy Ensemble (EE)—were applied to detect NCD status based on image features. The EE classifier achieved the best detection performance, with an accuracy of 0.90, precision of 0.64, and recall of 0.82, effectively handling data imbalance. Notably, the results also demonstrated that NCD onset could be predicted up to one day prior to clinical manifestation by training classifiers on pre-symptomatic suckle pressure data and testing on post-onset data. The EE classifier also outperformed other models in this early prediction window, with an accuracy of 0.74, precision of 0.67, and recall of 0.70. The results of our preliminary study suggest that suckle pressure may offer a novel, non-invasive approach for precision health monitoring in dairy systems, enabling timely intervention to reduce disease severity, improve calf health, and minimize economic losses. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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22 pages, 3981 KB

Open AccessArticle

Individual Recognition of a Group Beef Cattle Based on Improved YOLO v5

by Ziruo Li, Yadan Zhang, Xi Kang, Tianci Mao, Yanbin Li and Gang Liu

Agriculture 2025, 15(13), 1391; https://doi.org/10.3390/agriculture15131391 - 28 Jun 2025

Cited by 3 | Viewed by 1404

Abstract

Deep learning-based individual recognition of beef cattle has improved the efficiency and effectiveness of individual recognition, providing technical support for modern large-scale farms. However, issues such as over-reliance on back patterns, similar patterns of adjacent cattle leading to low recognition accuracy, and difficulties [...] Read more.

Deep learning-based individual recognition of beef cattle has improved the efficiency and effectiveness of individual recognition, providing technical support for modern large-scale farms. However, issues such as over-reliance on back patterns, similar patterns of adjacent cattle leading to low recognition accuracy, and difficulties in deploying models on edge devices exist in the process of group cattle recognition. In this study, we proposed a model based on improved YOLO v5. Specifically, a Simple, Parameter-Free (SimAM) attention module is connected with the residual network and Multidimensional Collaborative Attention mechanism (MCA) to obtain the MCA-SimAM-Resnet (MRS-ATT) module, enhancing the model’s feature extraction and expression capabilities. Then, the

L_{M P D I o U}

loss function is used to improve the localization accuracy of bounding boxes during target detection. Finally, structural pruning is applied to the model to achieve a lightweight version of the improved YOLO v5. Using 211 test images, the improved YOLO v5 model achieved an individual recognition precision (P) of 93.2%, recall (R) of 94.6%, mean Average Precision (mAP) of 94.5%, FLOPs of 7.84, 13.22 M parameters, and an average inference speed of 0.0746 s. The improved YOLO v5 model can accurately and quickly identify individuals within groups of cattle, with fewer parameters, making it easy to deploy on edge devices, thereby accelerating the development of intelligent cattle farming. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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27 pages, 21013 KB

Open AccessArticle

Improved YOLO-Goose-Based Method for Individual Identification of Lion-Head Geese and Egg Matching: Methods and Experimental Study

by Hengyuan Zhang, Zhenlong Wu, Tiemin Zhang, Canhuan Lu, Zhaohui Zhang, Jianzhou Ye, Jikang Yang, Degui Yang and Cheng Fang

Agriculture 2025, 15(13), 1345; https://doi.org/10.3390/agriculture15131345 - 23 Jun 2025

Cited by 2 | Viewed by 2487

Abstract

As a crucial characteristic waterfowl breed, the egg-laying performance of Lion-Headed Geese serves as a core indicator for precision breeding. Under large-scale flat rearing and selection practices, high phenotypic similarity among individuals within the same pedigree coupled with traditional manual observation and existing [...] Read more.

As a crucial characteristic waterfowl breed, the egg-laying performance of Lion-Headed Geese serves as a core indicator for precision breeding. Under large-scale flat rearing and selection practices, high phenotypic similarity among individuals within the same pedigree coupled with traditional manual observation and existing automation systems relying on fixed nesting boxes or RFID tags has posed challenges in achieving accurate goose–egg matching in dynamic environments, leading to inefficient individual selection. To address this, this study proposes YOLO-Goose, an improved YOLOv8s-based method, which designs five high-contrast neck rings (DoubleBar, Circle, Dot, Fence, Cylindrical) as individual identifiers. The method constructs a lightweight model with a small-object detection layer, integrates the GhostNet backbone to reduce parameter count by 67.2%, and employs the GIoU loss function to optimize neck ring localization accuracy. Experimental results show that the model achieves an F1 score of 93.8% and mAP50 of 96.4% on the self-built dataset, representing increases of 10.1% and 5% compared to the original YOLOv8s, with a 27.1% reduction in computational load. The dynamic matching algorithm, incorporating spatiotemporal trajectories and egg positional data, achieves a 95% matching rate, a 94.7% matching accuracy, and a 5.3% mismatching rate. Through lightweight deployment using TensorRT, the inference speed is enhanced by 1.4 times compared to PyTorch-1.12.1, with detection results uploaded to a cloud database in real time. This solution overcomes the technical bottleneck of individual selection in flat rearing environments, providing an innovative computer-vision-based approach for precision breeding of pedigree Lion-Headed Geese and offering significant engineering value for advancing intelligent waterfowl breeding. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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16 pages, 2853 KB

Open AccessArticle

Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms

by Xi Kang, Junjie Liang, Qian Li and Gang Liu

Agriculture 2025, 15(12), 1276; https://doi.org/10.3390/agriculture15121276 - 13 Jun 2025

Cited by 1 | Viewed by 2772

Abstract

Lameness significantly compromises dairy cattle welfare and productivity. Early detection enables prompt intervention, enhancing both animal health and farm efficiency. Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. This limitation is exacerbated by the [...] Read more.

Lameness significantly compromises dairy cattle welfare and productivity. Early detection enables prompt intervention, enhancing both animal health and farm efficiency. Current computer vision approaches often rely on isolated lameness feature quantification, disregarding critical interdependencies among gait parameters. This limitation is exacerbated by the distinct kinematic patterns exhibited across lameness severity grades, ultimately reducing detection accuracy. This study presents an integrated computer vision and deep-learning framework for dairy cattle lameness detection and severity classification. The proposed system comprises (1) a Cow Lameness Feature Map (CLFM) model extracting holistic gait kinematics (hoof trajectories and dorsal contour) from walking sequences, and (2) a DenseNet-Integrated Convolutional Attention Module (DCAM) that mitigates inter-individual variability through multi-feature fusion. Experimental validation utilized 3150 annotated lameness feature maps derived from 175 Holsteins under natural walking conditions, demonstrating robust classification performance. The classification accuracy of the method for varying degrees of lameness was 92.80%, the sensitivity was 89.21%, and the specificity was 94.60%. The detection of healthy and lameness dairy cows’ accuracy was 99.05%, the sensitivity was 100%, and the specificity was 98.57%. The experimental results demonstrate the advantage of implementing lameness severity-adaptive feature weighting through hierarchical network architecture. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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13 pages, 2855 KB

Open AccessArticle

Research on Video Behavior Detection and Analysis Model for Sow Estrus Cycle Based on Deep Learning

by Kaidong Lei, Bugao Li, Shan Zhong, Hua Yang, Hao Wang, Xiangfang Tang and Benhai Xiong

Agriculture 2025, 15(9), 975; https://doi.org/10.3390/agriculture15090975 - 30 Apr 2025

Cited by 4 | Viewed by 2102

Abstract

Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep [...] Read more.

Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep learning, more technologies are being integrated into smart agriculture. Intelligent large-scale pig farming has become an effective means to improve sow quality and productivity, with behavior recognition technology playing a crucial role in intelligent pig farming. Specifically, monitoring sow behavior enables an effective assessment of health conditions and welfare levels, ensuring efficient and healthy sow production. This study constructs a 3D-CNN model based on video data from the sow estrus cycle, achieving analysis of SOB, SOC, SOS, and SOW behaviors. In typical behavior classification, the model attains accuracy, recall, and F1-score values of (1.00, 0.90, 0.95; 0.96, 0.98, 0.97; 1.00, 0.96, 0.98; 0.86, 1.00, 0.93), respectively. Additionally, under conditions of multi-pig interference and non-specifically labeled data, the accuracy, recall, and F1-scores for the semantic recognition of SOB, SOC, SOS, and SOW behaviors based on the 3D-CNN model are (1.00, 0.90, 0.95; 0.89, 0.89, 0.89; 0.91, 1.00, 0.95; 1.00, 1.00, 1.00), respectively. These findings provide key technical support for establishing the classification and semantic recognition of typical sow behaviors during the estrus cycle, while also offering a practical solution for rapid video-based behavior detection and welfare monitoring in precision livestock farming. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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27 pages, 7107 KB

Open AccessSystematic Review

Computer Vision-Based Detection of Agonistic Behaviors in Pigs: Advances and Applications for Precision Livestock Farming

by Md Kamrul Hasan, Hong-Seok Mun, Ahsan Mehtab, Jin-Gu Kang, Md Sharifuzzaman, Eddiemar B. Lagua, Young-Hwa Kim, Hae-Rang Park and Chul-Ju Yang

Agriculture 2026, 16(6), 700; https://doi.org/10.3390/agriculture16060700 - 20 Mar 2026

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Abstract

Agonistic behaviors such as aggression, ear biting, and tail biting remain major challenges for pig welfare, particularly during the weaning and growing periods. Computer vision (CV) technologies are emerging as scalable tools for non-invasive monitoring of these behaviors. This systematic review summarizes recent [...] Read more.

Agonistic behaviors such as aggression, ear biting, and tail biting remain major challenges for pig welfare, particularly during the weaning and growing periods. Computer vision (CV) technologies are emerging as scalable tools for non-invasive monitoring of these behaviors. This systematic review summarizes recent advances in CV-based detection of agonistic behaviors in pigs and identifies factors influencing their reliability and commercial adoption. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, a structured search of Scopus, Web of Science, and PubMed identified 42 eligible studies. Most studies employ deep learning approaches, including you only look once (YOLO)-based detectors and spatio-temporal models, achieving detection accuracy of up to 97% for behaviors such as head knocking, head-to-body pushing, and tail biting, typically evaluated under controlled conditions using mAP@0.5. Three key findings emerged: rapid progress in deep learning-based detection; methodological heterogeneity in behavioral definitions, validation strategies, and annotation protocols; and a gap between high detection accuracy and demonstrated improvements in welfare or productivity. Progress is limited by scarce cross-farm validation, inconsistent bout definitions, reliance on manual annotations, and weak integration with physiological and production indicators. Future research should prioritize standardized behavioral definitions, multimodal integration, predictive modeling, and rigorous external validation. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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