Evaluation of piglet suckling competition index based on YOLOv10 and optical flow direction distribution features
With the rapid development of intelligent farming technologies, effectively evaluating piglet competition behaviour during the suckling period has become a key research focus for enhancing livestock management. This paper presents a method for evaluating the piglet suckling competition index, which...
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| Veröffentlicht in: | Biosystems engineering Jg. 257; S. 104197 |
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| ISSN: | 1537-5110 |
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| Abstract | With the rapid development of intelligent farming technologies, effectively evaluating piglet competition behaviour during the suckling period has become a key research focus for enhancing livestock management. This paper presents a method for evaluating the piglet suckling competition index, which integrates the YOLOv10 object detection algorithm and optical flow direction distribution features. First, the YOLOv10 model is employed to detect the sow's posture and the positions of the piglets, classifying the sow's posture into lateral recumbency and other postures. Subsequently, precise localisation of the lactation period is achieved by calculating the mask ratio of the piglets within the sow's region and the changes in group activity. Finally, the Farneback optical flow algorithm is utilised to analyse the direction distribution of the optical flow within the piglet region, and the variation coefficient of information entropy is employed to quantify the intensity of piglet suckling competition. Experimental results demonstrate that the proposed method performs well in both object detection and behaviour localisation, achieving a precision of 91.51 % and a recall of 96.04 % for lactation period localisation. Additionally, the method successfully validated the evaluation of piglet suckling competition in different test pens. This study provides technical support for intelligent farming technologies, helping to optimise piglet nutrition management and enhance farming efficiency.
•Objectively assesses piglet suckling rivalry, independent of weight or activity.•YOLOv10 detects sow posture and piglet positions for behaviour analysis.•Sow nursing precisely located using piglet mask ratio and group activity.•Optical flow and entropy variation quantify suckling competition intensity. |
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| AbstractList | With the rapid development of intelligent farming technologies, effectively evaluating piglet competition behaviour during the suckling period has become a key research focus for enhancing livestock management. This paper presents a method for evaluating the piglet suckling competition index, which integrates the YOLOv10 object detection algorithm and optical flow direction distribution features. First, the YOLOv10 model is employed to detect the sow's posture and the positions of the piglets, classifying the sow's posture into lateral recumbency and other postures. Subsequently, precise localisation of the lactation period is achieved by calculating the mask ratio of the piglets within the sow's region and the changes in group activity. Finally, the Farneback optical flow algorithm is utilised to analyse the direction distribution of the optical flow within the piglet region, and the variation coefficient of information entropy is employed to quantify the intensity of piglet suckling competition. Experimental results demonstrate that the proposed method performs well in both object detection and behaviour localisation, achieving a precision of 91.51 % and a recall of 96.04 % for lactation period localisation. Additionally, the method successfully validated the evaluation of piglet suckling competition in different test pens. This study provides technical support for intelligent farming technologies, helping to optimise piglet nutrition management and enhance farming efficiency. With the rapid development of intelligent farming technologies, effectively evaluating piglet competition behaviour during the suckling period has become a key research focus for enhancing livestock management. This paper presents a method for evaluating the piglet suckling competition index, which integrates the YOLOv10 object detection algorithm and optical flow direction distribution features. First, the YOLOv10 model is employed to detect the sow's posture and the positions of the piglets, classifying the sow's posture into lateral recumbency and other postures. Subsequently, precise localisation of the lactation period is achieved by calculating the mask ratio of the piglets within the sow's region and the changes in group activity. Finally, the Farneback optical flow algorithm is utilised to analyse the direction distribution of the optical flow within the piglet region, and the variation coefficient of information entropy is employed to quantify the intensity of piglet suckling competition. Experimental results demonstrate that the proposed method performs well in both object detection and behaviour localisation, achieving a precision of 91.51 % and a recall of 96.04 % for lactation period localisation. Additionally, the method successfully validated the evaluation of piglet suckling competition in different test pens. This study provides technical support for intelligent farming technologies, helping to optimise piglet nutrition management and enhance farming efficiency. •Objectively assesses piglet suckling rivalry, independent of weight or activity.•YOLOv10 detects sow posture and piglet positions for behaviour analysis.•Sow nursing precisely located using piglet mask ratio and group activity.•Optical flow and entropy variation quantify suckling competition intensity. |
| ArticleNumber | 104197 |
| Author | Liu, Luo Li, Peng Shen, Mingxia Chen, Jinxin Yao, Wen Liu, Longshen |
| Author_xml | – sequence: 1 givenname: Jinxin surname: Chen fullname: Chen, Jinxin organization: College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China – sequence: 2 givenname: Luo surname: Liu fullname: Liu, Luo organization: College of Veterinary Medicine, Nanjing Agricultural University, Nanjing, 210095, China – sequence: 3 givenname: Peng surname: Li fullname: Li, Peng organization: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China – sequence: 4 givenname: Wen surname: Yao fullname: Yao, Wen organization: College of Animal Science & Technology, Nanjing Agricultural University, Nanjing, 210095, China – sequence: 5 givenname: Mingxia surname: Shen fullname: Shen, Mingxia organization: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China – sequence: 6 givenname: Longshen orcidid: 0000-0002-6085-8047 surname: Liu fullname: Liu, Longshen email: liulongshen@njau.edu.cn organization: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China |
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| Cites_doi | 10.1016/j.jveb.2014.01.005 10.1016/j.compag.2024.109243 10.1016/j.compag.2022.107162 10.1109/TPAMI.2018.2858826 10.1016/j.biosystemseng.2017.06.014 10.1016/j.compag.2024.109716 10.3390/e21060608 10.1016/j.compag.2022.107423 10.1016/j.compag.2022.106741 10.1016/j.compag.2021.106384 10.1016/j.compag.2024.109090 10.1016/j.biosystemseng.2022.03.005 10.1016/j.yhbeh.2007.03.022 10.3390/ani11061607 10.1016/j.compag.2023.107938 10.1016/j.compag.2021.106139 10.1016/j.scitotenv.2023.162730 10.1016/j.cviu.2016.10.018 10.1016/j.applanim.2014.09.005 10.1016/j.compag.2023.107877 10.1016/j.biosystemseng.2018.09.011 10.1016/j.biosystemseng.2022.03.006 10.1016/j.compag.2021.106376 10.1016/j.cviu.2015.02.008 10.1016/0004-3702(81)90024-2 10.1007/s00265-010-1128-4 10.1016/j.applanim.2012.11.014 10.1016/j.compag.2019.105048 10.1016/j.compag.2023.108167 10.1016/j.applanim.2019.03.001 10.1016/j.compag.2021.106357 |
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| Keywords | Behavioural analysis Sow posture recognition Intelligent farming Information entropy Lactation monitoring |
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| References | Fortun, Bouthemy, Kervrann (bib12) 2015; 134 Ho, Tsai, Kuo (bib20) 2021; 189 Verdon, Morrison, Rault (bib36) 2019; 214 Gan, Ou, Li, Wang, Guo, Mao (bib16) 2022; 199 Ding, Chen, Shen, Liu (bib8) 2022; 194 Liu, Anguelov, Erhan, Szegedy, Reed, Fu (bib28) 2016 Law, Deng (bib24) 2018 Jocher, Chaurasia, Qiu (bib23) 2023 Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez (bib35) 2017 Chollet (bib7) 2017 Zhang, Lin, Zheng, Tang, Fang, Yu (bib43) 2019; 21 Lin, Goyal, Girshick, He, Dollár (bib27) 2020; 42 Liu, Qi, Qin, Shi, Jia (bib29) 2018 Gan, Xu, Hou, Guo, Liu, Xue (bib17) 2022; 217 He, Guo, Guo, Lyu, Huang, Mao (bib19) 2025; 229 Yang, Huang, Yang, Li, Chen, Gan (bib40) 2019; 167 Loshchilov, Hutter (bib30) 2019 Farnebäck (bib10) 2003 Gan, Guo, Liu, Deng, Zhou, Luo (bib13) 2023; 210 Gan, Ou, Huang, Xu, Li, Li (bib15) 2021; 188 Skok, Prevolnik, Urek, Mesarec, Škorjanc (bib32) 2014; 161 Li, Zhang, Lv, Han, Jiang, Song (bib26) 2022; 218 Bradski (bib6) 2000 Feng, Zhong, Gao, Scott, Huang (bib11) 2021 Idrees, Zamir, Jiang, Gorban, Laptev, Sukthankar (bib22) 2017; 155 Algers, Uvnäs-Moberg (bib1) 2007; 52 Besteiro, Arango, Ortega, Fernández, Rodríguez (bib4) 2021; 11 Han, Liu, Li, Jiang, Ma, Chu (bib18) 2023; 877 Van Beirendonck, Van Thielen, Verbeke, Driessen (bib34) 2014; 9 Besteiro, Arango, Rodríguez, Fernández, Velo (bib5) 2018; 173 Badgujar, Poulose, Gan (bib3) 2024; 223 Horn, Schunck (bib21) 1981; 17 Yang, Huang, Zhu, Yang, Chen, Li (bib41) 2018; 175 Yang, Zheng, Zou, Gan, Li, Huang (bib42) 2021; 185 Andersen, Nævdal, Bøe (bib2) 2011; 65 Lucas, Kanade (bib31) 1981; 2 Wang, Chen, Liu, Chen, Lin, Han (bib37) 2024 Yan, Dai, Liu, Yin, Li, Wu (bib38) 2024; 224 Yang, Chen, Xu, Shen, Li, Norton (bib39) 2023; 213 Gan, Li, Ou, Yang, Huang, Liu (bib14) 2021; 189 Skok, Škorjanc (bib33) 2013; 144 Ding, Liu, Lu, Liu, Chen, Shen (bib9) 2022; 202 Li, Xu, Chen, Cheng, Shen (bib25) 2023; 210 Gan (10.1016/j.biosystemseng.2025.104197_bib16) 2022; 199 Jocher (10.1016/j.biosystemseng.2025.104197_bib23) Yang (10.1016/j.biosystemseng.2025.104197_bib42) 2021; 185 Gan (10.1016/j.biosystemseng.2025.104197_bib15) 2021; 188 Yang (10.1016/j.biosystemseng.2025.104197_bib41) 2018; 175 Besteiro (10.1016/j.biosystemseng.2025.104197_bib5) 2018; 173 Loshchilov (10.1016/j.biosystemseng.2025.104197_bib30) 2019 Van Beirendonck (10.1016/j.biosystemseng.2025.104197_bib34) 2014; 9 Algers (10.1016/j.biosystemseng.2025.104197_bib1) 2007; 52 Skok (10.1016/j.biosystemseng.2025.104197_bib32) 2014; 161 He (10.1016/j.biosystemseng.2025.104197_bib19) 2025; 229 Yang (10.1016/j.biosystemseng.2025.104197_bib39) 2023; 213 Ding (10.1016/j.biosystemseng.2025.104197_bib8) 2022; 194 Zhang (10.1016/j.biosystemseng.2025.104197_bib43) 2019; 21 Skok (10.1016/j.biosystemseng.2025.104197_bib33) 2013; 144 Yan (10.1016/j.biosystemseng.2025.104197_bib38) 2024; 224 Verdon (10.1016/j.biosystemseng.2025.104197_bib36) 2019; 214 Chollet (10.1016/j.biosystemseng.2025.104197_bib7) 2017 Ding (10.1016/j.biosystemseng.2025.104197_bib9) 2022; 202 Liu (10.1016/j.biosystemseng.2025.104197_bib28) 2016 Ho (10.1016/j.biosystemseng.2025.104197_bib20) 2021; 189 Horn (10.1016/j.biosystemseng.2025.104197_bib21) 1981; 17 Lucas (10.1016/j.biosystemseng.2025.104197_bib31) 1981; 2 Idrees (10.1016/j.biosystemseng.2025.104197_bib22) 2017; 155 Gan (10.1016/j.biosystemseng.2025.104197_bib14) 2021; 189 Bradski (10.1016/j.biosystemseng.2025.104197_bib6) 2000 Badgujar (10.1016/j.biosystemseng.2025.104197_bib3) 2024; 223 Law (10.1016/j.biosystemseng.2025.104197_bib24) 2018 Vaswani (10.1016/j.biosystemseng.2025.104197_bib35) 2017 Feng (10.1016/j.biosystemseng.2025.104197_bib11) 2021 Wang (10.1016/j.biosystemseng.2025.104197_bib37) 2024 Liu (10.1016/j.biosystemseng.2025.104197_bib29) 2018 Andersen (10.1016/j.biosystemseng.2025.104197_bib2) 2011; 65 Yang (10.1016/j.biosystemseng.2025.104197_bib40) 2019; 167 Gan (10.1016/j.biosystemseng.2025.104197_bib13) 2023; 210 Farnebäck (10.1016/j.biosystemseng.2025.104197_bib10) 2003 Gan (10.1016/j.biosystemseng.2025.104197_bib17) 2022; 217 Li (10.1016/j.biosystemseng.2025.104197_bib25) 2023; 210 Fortun (10.1016/j.biosystemseng.2025.104197_bib12) 2015; 134 Li (10.1016/j.biosystemseng.2025.104197_bib26) 2022; 218 Lin (10.1016/j.biosystemseng.2025.104197_bib27) 2020; 42 Besteiro (10.1016/j.biosystemseng.2025.104197_bib4) 2021; 11 Han (10.1016/j.biosystemseng.2025.104197_bib18) 2023; 877 |
| References_xml | – volume: 218 start-page: 62 year: 2022 end-page: 77 ident: bib26 article-title: Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows publication-title: Biosystems Engineering – volume: 213 year: 2023 ident: bib39 article-title: Recognizing the rooting action of prepartum sow in free-farrowing pen using computer vision publication-title: Computers and Electronics in Agriculture – year: 2017 ident: bib7 article-title: Xception: Deep learning with depthwise separable convolutions publication-title: 2017 IEEE conference on computer vision and pattern recognition (CVPR) – year: 2021 ident: bib11 article-title: Tood: Task-Aligned one-stage object detection publication-title: 2021 IEEE/CVF international conference on computer vision – volume: 161 start-page: 42 year: 2014 end-page: 50 ident: bib32 article-title: Behavioural patterns established during suckling reappear when piglets are forced to form a new dominance hierarchy publication-title: Applied Animal Behaviour Science – volume: 134 start-page: 1 year: 2015 end-page: 21 ident: bib12 article-title: Optical flow modeling and computation: A survey publication-title: Computer Vision and Image Understanding – volume: 223 year: 2024 ident: bib3 article-title: Agricultural object detection with you only look once (YOLO) algorithm: A bibliometric and systematic literature review publication-title: Computers and Electronics in Agriculture – volume: 194 year: 2022 ident: bib8 article-title: Activity detection of suckling piglets based on motion area analysis using frame differences in combination with convolution neural network publication-title: Computers and Electronics in Agriculture – year: 2023 ident: bib23 article-title: Ultralytics YOLO – volume: 217 start-page: 102 year: 2022 end-page: 114 ident: bib17 article-title: Spatiotemporal graph convolutional network for automated detection and analysis of social behaviours among pre-weaning piglets publication-title: Biosystems Engineering – volume: 175 start-page: 133 year: 2018 end-page: 145 ident: bib41 article-title: Automatic recognition of sow nursing behaviour using deep learning-based segmentation and spatial and temporal features publication-title: Biosystems Engineering – volume: 189 year: 2021 ident: bib20 article-title: Automatic monitoring of lactation frequency of sows and movement quantification of newborn piglets in farrowing houses using convolutional neural networks publication-title: Computers and Electronics in Agriculture – volume: 2 start-page: 674 year: 1981 end-page: 679 ident: bib31 article-title: An iterative image registration technique with an application to stereo vision publication-title: IJCAI’81: 7th International Joint Conference on Artificial Intelligence – volume: 155 start-page: 1 year: 2017 end-page: 23 ident: bib22 article-title: The THUMOS challenge on action recognition for videos “in the wild.” publication-title: Computer Vision and Image Understanding – volume: 224 year: 2024 ident: bib38 article-title: Deep neural network with adaptive dual-modality fusion for temporal aggressive behavior detection of group-housed pigs publication-title: Computers and Electronics in Agriculture – volume: 199 year: 2022 ident: bib16 article-title: Automated detection and analysis of piglet suckling behaviour using high-accuracy amodal instance segmentation publication-title: Computers and Electronics in Agriculture – year: 2016 ident: bib28 article-title: Ssd: Single shot MultiBox detector publication-title: Computer vision – ECCV 2016 – volume: 877 year: 2023 ident: bib18 article-title: Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight publication-title: Science of The Total Environment – volume: 9 start-page: 107 year: 2014 end-page: 113 ident: bib34 article-title: The association between sow and piglet behavior publication-title: Journal of Veterinary Behavior – volume: 214 start-page: 25 year: 2019 end-page: 33 ident: bib36 article-title: Sow and piglet behaviour in group lactation housing from 7 or 14 days post-partum publication-title: Applied Animal Behaviour Science – volume: 65 start-page: 1159 year: 2011 end-page: 1167 ident: bib2 article-title: Maternal investment, sibling competition, and offspring survival with increasing litter size and parity in pigs ( publication-title: Behavioral Ecology and Sociobiology – volume: 229 year: 2025 ident: bib19 article-title: Characterization of social cohesion status of pre-weaning piglets based on lightweight pose estimation publication-title: Computers and Electronics in Agriculture – volume: 210 year: 2023 ident: bib13 article-title: Counting piglet suckling events using deep learning-based action density estimation publication-title: Computers and Electronics in Agriculture – volume: 210 year: 2023 ident: bib25 article-title: Recognition of fine-grained sow nursing behavior based on the SlowFast and hidden Markov models publication-title: Computers and Electronics in Agriculture – volume: 173 start-page: 85 year: 2018 end-page: 92 ident: bib5 article-title: Estimation of patterns in weaned piglets' activity using spectral analysis publication-title: Biosystems Engineering – year: 2000 ident: bib6 article-title: The OpenCV library publication-title: Dr. Dobb’s Journal of Software Tools – volume: 17 start-page: 185 year: 1981 end-page: 203 ident: bib21 article-title: Determining optical flow publication-title: Artificial Intelligence – volume: 42 start-page: 318 year: 2020 end-page: 327 ident: bib27 article-title: Focal loss for dense object detection publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 1 year: 2019 end-page: 18 ident: bib30 article-title: Decoupled weight decay regularization publication-title: 7th International Conference on Learning Representations, ICLR – volume: 185 year: 2021 ident: bib42 article-title: A CNN-based posture change detection for lactating sow in untrimmed depth videos publication-title: Computers and Electronics in Agriculture – year: 2003 ident: bib10 article-title: Two-frame motion estimation based on polynomial expansion publication-title: Proceedings of the 13th scandinavian conference on image analysis – volume: 144 start-page: 39 year: 2013 end-page: 45 ident: bib33 article-title: Formation of teat order and estimation of piglets' distribution along the mammary complex using mid-domain effect (MDE) model publication-title: Applied Animal Behaviour Science – year: 2018 ident: bib24 article-title: CornerNet: Detecting objects as paired keypoints publication-title: Computer vision – ECCV 2018 – year: 2018 ident: bib29 article-title: Path aggregation network for instance segmentation publication-title: 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR) – volume: 189 year: 2021 ident: bib14 article-title: Fast and accurate detection of lactating sow nursing behavior with CNN-based optical flow and features publication-title: Computers and Electronics in Agriculture – volume: 167 year: 2019 ident: bib40 article-title: Automated video analysis of sow nursing behavior based on fully convolutional network and oriented optical flow publication-title: Computers and Electronics in Agriculture – volume: 11 year: 2021 ident: bib4 article-title: Long-term measurement of piglet activity using passive infrared detectors publication-title: Animals – volume: 188 year: 2021 ident: bib15 article-title: Automated detection and analysis of social behaviors among preweaning piglets using key point-based spatial and temporal features publication-title: Computers and Electronics in Agriculture – volume: 52 start-page: 78 year: 2007 end-page: 85 ident: bib1 article-title: Maternal behavior in pigs publication-title: Hormones and Behavior – year: 2024 ident: bib37 article-title: YOLOv10: Real-Time end-to-end object detection publication-title: arXiv E-Prints – volume: 202 year: 2022 ident: bib9 article-title: Social density detection for suckling piglets based on convolutional neural network combined with local outlier factor algorithm publication-title: Computers and Electronics in Agriculture – volume: 21 year: 2019 ident: bib43 article-title: Detection of salient crowd motion based on repulsive force network and direction entropy publication-title: Entropy – year: 2017 ident: bib35 article-title: Attention is all you need publication-title: Proceedings of the 31st international conference on neural information processing systems – year: 2016 ident: 10.1016/j.biosystemseng.2025.104197_bib28 article-title: Ssd: Single shot MultiBox detector – year: 2018 ident: 10.1016/j.biosystemseng.2025.104197_bib24 article-title: CornerNet: Detecting objects as paired keypoints – volume: 9 start-page: 107 issue: 3 year: 2014 ident: 10.1016/j.biosystemseng.2025.104197_bib34 article-title: The association between sow and piglet behavior publication-title: Journal of Veterinary Behavior doi: 10.1016/j.jveb.2014.01.005 – year: 2024 ident: 10.1016/j.biosystemseng.2025.104197_bib37 article-title: YOLOv10: Real-Time end-to-end object detection publication-title: arXiv E-Prints – volume: 224 year: 2024 ident: 10.1016/j.biosystemseng.2025.104197_bib38 article-title: Deep neural network with adaptive dual-modality fusion for temporal aggressive behavior detection of group-housed pigs publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2024.109243 – volume: 199 year: 2022 ident: 10.1016/j.biosystemseng.2025.104197_bib16 article-title: Automated detection and analysis of piglet suckling behaviour using high-accuracy amodal instance segmentation publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.107162 – volume: 2 start-page: 674 year: 1981 ident: 10.1016/j.biosystemseng.2025.104197_bib31 article-title: An iterative image registration technique with an application to stereo vision publication-title: IJCAI’81: 7th International Joint Conference on Artificial Intelligence – ident: 10.1016/j.biosystemseng.2025.104197_bib23 – volume: 42 start-page: 318 issue: 2 year: 2020 ident: 10.1016/j.biosystemseng.2025.104197_bib27 article-title: Focal loss for dense object detection publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2018.2858826 – volume: 173 start-page: 85 year: 2018 ident: 10.1016/j.biosystemseng.2025.104197_bib5 article-title: Estimation of patterns in weaned piglets' activity using spectral analysis publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2017.06.014 – volume: 229 year: 2025 ident: 10.1016/j.biosystemseng.2025.104197_bib19 article-title: Characterization of social cohesion status of pre-weaning piglets based on lightweight pose estimation publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2024.109716 – year: 2017 ident: 10.1016/j.biosystemseng.2025.104197_bib35 article-title: Attention is all you need – volume: 21 issue: 6 year: 2019 ident: 10.1016/j.biosystemseng.2025.104197_bib43 article-title: Detection of salient crowd motion based on repulsive force network and direction entropy publication-title: Entropy doi: 10.3390/e21060608 – volume: 202 year: 2022 ident: 10.1016/j.biosystemseng.2025.104197_bib9 article-title: Social density detection for suckling piglets based on convolutional neural network combined with local outlier factor algorithm publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.107423 – volume: 194 year: 2022 ident: 10.1016/j.biosystemseng.2025.104197_bib8 article-title: Activity detection of suckling piglets based on motion area analysis using frame differences in combination with convolution neural network publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.106741 – volume: 189 year: 2021 ident: 10.1016/j.biosystemseng.2025.104197_bib14 article-title: Fast and accurate detection of lactating sow nursing behavior with CNN-based optical flow and features publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106384 – volume: 223 year: 2024 ident: 10.1016/j.biosystemseng.2025.104197_bib3 article-title: Agricultural object detection with you only look once (YOLO) algorithm: A bibliometric and systematic literature review publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2024.109090 – volume: 217 start-page: 102 year: 2022 ident: 10.1016/j.biosystemseng.2025.104197_bib17 article-title: Spatiotemporal graph convolutional network for automated detection and analysis of social behaviours among pre-weaning piglets publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2022.03.005 – year: 2003 ident: 10.1016/j.biosystemseng.2025.104197_bib10 article-title: Two-frame motion estimation based on polynomial expansion – volume: 52 start-page: 78 issue: 1 year: 2007 ident: 10.1016/j.biosystemseng.2025.104197_bib1 article-title: Maternal behavior in pigs publication-title: Hormones and Behavior doi: 10.1016/j.yhbeh.2007.03.022 – volume: 11 issue: 6 year: 2021 ident: 10.1016/j.biosystemseng.2025.104197_bib4 article-title: Long-term measurement of piglet activity using passive infrared detectors publication-title: Animals doi: 10.3390/ani11061607 – volume: 210 year: 2023 ident: 10.1016/j.biosystemseng.2025.104197_bib25 article-title: Recognition of fine-grained sow nursing behavior based on the SlowFast and hidden Markov models publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2023.107938 – volume: 185 year: 2021 ident: 10.1016/j.biosystemseng.2025.104197_bib42 article-title: A CNN-based posture change detection for lactating sow in untrimmed depth videos publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106139 – volume: 877 year: 2023 ident: 10.1016/j.biosystemseng.2025.104197_bib18 article-title: Novel risk assessment model of food quality and safety considering physical-chemical and pollutant indexes based on coefficient of variance integrating entropy weight publication-title: Science of The Total Environment doi: 10.1016/j.scitotenv.2023.162730 – volume: 155 start-page: 1 year: 2017 ident: 10.1016/j.biosystemseng.2025.104197_bib22 article-title: The THUMOS challenge on action recognition for videos “in the wild.” publication-title: Computer Vision and Image Understanding doi: 10.1016/j.cviu.2016.10.018 – volume: 161 start-page: 42 year: 2014 ident: 10.1016/j.biosystemseng.2025.104197_bib32 article-title: Behavioural patterns established during suckling reappear when piglets are forced to form a new dominance hierarchy publication-title: Applied Animal Behaviour Science doi: 10.1016/j.applanim.2014.09.005 – volume: 210 year: 2023 ident: 10.1016/j.biosystemseng.2025.104197_bib13 article-title: Counting piglet suckling events using deep learning-based action density estimation publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2023.107877 – volume: 175 start-page: 133 year: 2018 ident: 10.1016/j.biosystemseng.2025.104197_bib41 article-title: Automatic recognition of sow nursing behaviour using deep learning-based segmentation and spatial and temporal features publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2018.09.011 – year: 2000 ident: 10.1016/j.biosystemseng.2025.104197_bib6 article-title: The OpenCV library publication-title: Dr. Dobb’s Journal of Software Tools – volume: 218 start-page: 62 year: 2022 ident: 10.1016/j.biosystemseng.2025.104197_bib26 article-title: Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2022.03.006 – volume: 189 year: 2021 ident: 10.1016/j.biosystemseng.2025.104197_bib20 article-title: Automatic monitoring of lactation frequency of sows and movement quantification of newborn piglets in farrowing houses using convolutional neural networks publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106376 – year: 2018 ident: 10.1016/j.biosystemseng.2025.104197_bib29 article-title: Path aggregation network for instance segmentation – year: 2021 ident: 10.1016/j.biosystemseng.2025.104197_bib11 article-title: Tood: Task-Aligned one-stage object detection – volume: 134 start-page: 1 year: 2015 ident: 10.1016/j.biosystemseng.2025.104197_bib12 article-title: Optical flow modeling and computation: A survey publication-title: Computer Vision and Image Understanding doi: 10.1016/j.cviu.2015.02.008 – volume: 17 start-page: 185 issue: 1 year: 1981 ident: 10.1016/j.biosystemseng.2025.104197_bib21 article-title: Determining optical flow publication-title: Artificial Intelligence doi: 10.1016/0004-3702(81)90024-2 – volume: 65 start-page: 1159 year: 2011 ident: 10.1016/j.biosystemseng.2025.104197_bib2 article-title: Maternal investment, sibling competition, and offspring survival with increasing litter size and parity in pigs (Sus scrofa) publication-title: Behavioral Ecology and Sociobiology doi: 10.1007/s00265-010-1128-4 – volume: 144 start-page: 39 issue: 1 year: 2013 ident: 10.1016/j.biosystemseng.2025.104197_bib33 article-title: Formation of teat order and estimation of piglets' distribution along the mammary complex using mid-domain effect (MDE) model publication-title: Applied Animal Behaviour Science doi: 10.1016/j.applanim.2012.11.014 – volume: 167 year: 2019 ident: 10.1016/j.biosystemseng.2025.104197_bib40 article-title: Automated video analysis of sow nursing behavior based on fully convolutional network and oriented optical flow publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2019.105048 – volume: 213 year: 2023 ident: 10.1016/j.biosystemseng.2025.104197_bib39 article-title: Recognizing the rooting action of prepartum sow in free-farrowing pen using computer vision publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2023.108167 – start-page: 1 year: 2019 ident: 10.1016/j.biosystemseng.2025.104197_bib30 article-title: Decoupled weight decay regularization publication-title: 7th International Conference on Learning Representations, ICLR – volume: 214 start-page: 25 year: 2019 ident: 10.1016/j.biosystemseng.2025.104197_bib36 article-title: Sow and piglet behaviour in group lactation housing from 7 or 14 days post-partum publication-title: Applied Animal Behaviour Science doi: 10.1016/j.applanim.2019.03.001 – year: 2017 ident: 10.1016/j.biosystemseng.2025.104197_bib7 article-title: Xception: Deep learning with depthwise separable convolutions – volume: 188 year: 2021 ident: 10.1016/j.biosystemseng.2025.104197_bib15 article-title: Automated detection and analysis of social behaviors among preweaning piglets using key point-based spatial and temporal features publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106357 |
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| SubjectTerms | algorithms Behavioural analysis entropy Information entropy Intelligent farming lactation Lactation monitoring livestock husbandry nutrition piglets posture Sow posture recognition |
| Title | Evaluation of piglet suckling competition index based on YOLOv10 and optical flow direction distribution features |
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