Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming
| dc.contributor.author | Arıkan, İ. | |
| dc.contributor.author | Ayav, T. | |
| dc.contributor.author | Seçkin, A.Ç. | |
| dc.contributor.author | Soygazi, F. | |
| dc.date.accessioned | 2024-01-30T09:24:44Z | |
| dc.date.available | 2024-01-30T09:24:44Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors. | en_US |
| dc.identifier.doi | 10.3390/s23249795 | |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.scopus | 2-s2.0-85180616159 | |
| dc.identifier.uri | https://doi.org/10.3390/s23249795 | |
| dc.identifier.uri | https://hdl.handle.net/11147/14259 | |
| dc.language.iso | en | en_US |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | en_US |
| dc.relation.ispartof | Sensors | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | augmented reality | en_US |
| dc.subject | dairy cow identification | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | estrus detection | en_US |
| dc.subject | image processing | en_US |
| dc.subject | livestock | en_US |
| dc.subject | precision livestock farming | en_US |
| dc.subject | transfer learning | en_US |
| dc.subject | Augmented reality | en_US |
| dc.subject | Dairies | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Farms | en_US |
| dc.subject | Image segmentation | en_US |
| dc.subject | Losses | en_US |
| dc.subject | Dairy cow | en_US |
| dc.subject | Dairy cow identification | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Estrus detection | en_US |
| dc.subject | Images processing | en_US |
| dc.subject | Livestock | en_US |
| dc.subject | Precision livestock farming | en_US |
| dc.subject | Region-of-interest | en_US |
| dc.subject | Regions of interest | en_US |
| dc.subject | Transfer learning | en_US |
| dc.subject | Mountings | en_US |
| dc.subject | animal | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | augmented reality | en_US |
| dc.subject | bovine | en_US |
| dc.subject | dairying | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | estrus | en_US |
| dc.subject | female | en_US |
| dc.subject | livestock | en_US |
| dc.subject | milk | en_US |
| dc.subject | procedures | en_US |
| dc.subject | Animals | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Augmented Reality | en_US |
| dc.subject | Cattle | en_US |
| dc.subject | Dairying | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Estrus Detection | en_US |
| dc.subject | Female | en_US |
| dc.subject | Livestock | en_US |
| dc.subject | Milk | en_US |
| dc.title | Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
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| gdc.description.department | İzmir Institute of Technology | en_US |
| gdc.description.departmenttemp | Arıkan, İ., Computer Engineering Department, İzmir Institute of Technology, Izmir, 35430, Turkey; Ayav, T., Computer Engineering Department, İzmir Institute of Technology, Izmir, 35430, Turkey; Seçkin, A.Ç., Computer Engineering Department, Aydın Adnan Menderes University, Aydın, 09100, Turkey; Soygazi, F., Computer Engineering Department, Aydın Adnan Menderes University, Aydın, 09100, Turkey | en_US |
| gdc.description.issue | 24 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q1 | |
| gdc.description.volume | 23 | en_US |
| gdc.description.wosquality | Q2 | |
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| gdc.identifier.pmid | 38139641 | |
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| gdc.oaire.keywords | Livestock | |
| gdc.oaire.keywords | Augmented Reality | |
| gdc.oaire.keywords | Chemical technology | |
| gdc.oaire.keywords | deep learning | |
| gdc.oaire.keywords | TP1-1185 | |
| gdc.oaire.keywords | artificial intelligence | |
| gdc.oaire.keywords | augmented reality | |
| gdc.oaire.keywords | Article | |
| gdc.oaire.keywords | dairy cow identification | |
| gdc.oaire.keywords | image processing | |
| gdc.oaire.keywords | Dairying | |
| gdc.oaire.keywords | Deep Learning | |
| gdc.oaire.keywords | Milk | |
| gdc.oaire.keywords | Artificial Intelligence | |
| gdc.oaire.keywords | estrus detection | |
| gdc.oaire.keywords | Animals | |
| gdc.oaire.keywords | Female | |
| gdc.oaire.keywords | Cattle | |
| gdc.oaire.keywords | Estrus Detection | |
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| gdc.oaire.sciencefields | 04 agricultural and veterinary sciences | |
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| gdc.oaire.sciencefields | 0401 agriculture, forestry, and fisheries | |
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