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Agriculture
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Computer Vision Analysis Applied to Farm Animals
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Computer Vision Analysis Applied to Farm Animals

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2136

Special Issue Editors

Dr. Yang Zhao

E-Mail Website
Guest Editor
Department of Animal Science, University of Tennessee, Knoxville, TN 37996, USA
Interests: precision poultry farming that addresses challenges in poultry production regarding smart sensoring; robotics; behavior monitoring; welfare assessment; airborne transmission of pathogens; environment management
Special Issues, Collections and Topics in MDPI journals
Dr. Beibei Xu

E-Mail Website
Guest Editor
Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14850, USA
Interests: digital agriculture; precision farming; machine learning; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of computer vision and image analysis into precision livestock farming has revolutionized agricultural practices, offering innovative solutions for monitoring and managing farm animals. Historically, traditional animal husbandry relied heavily on manual inspection and intervention. However, recent advances in technology have introduced sophisticated image processing techniques that enable real-time, automated analysis. The aim of this Special Issue is to explore cutting-edge research and applications in computer vision and image analysis for precision livestock farming, emphasizing their role in improving animal health, welfare, and overall farm efficiency. We are soliciting papers that present novel methodologies, applications, and case studies in this field. Topics of interest include, but are not limited to, advanced imaging techniques for monitoring animal health, automated behavior analysis, and integration of computer vision systems with other precision livestock farming technologies. This Special Issue seeks to bring together innovative research and practical solutions to address current challenges and future opportunities in livestock farming.

Dr. Yang Zhao
Dr. Beibei Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • image analysis
  • precision livestock farming
  • animal welfare
  • deep learning
  • behavior monitoring
  • health assessment
  • data fusion
  • imaging technologies

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Published Papers (4 papers)

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Research

22 pages, 3981 KiB  
Article
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
Viewed by 137
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 LMPDIoU 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 KiB  
Article
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
Viewed by 378
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 KiB  
Article
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
Viewed by 384
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 KiB  
Article
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
Viewed by 469
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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