Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14259
Title: Estrus Detection and Dairy Cow Identification with Cascade Deep Learning for Augmented Reality-Ready Livestock Farming
Authors: Arıkan, İ.
Ayav, T.
Seçkin, A.Ç.
Soygazi, F.
Keywords: artificial intelligence
augmented reality
dairy cow identification
deep learning
estrus detection
image processing
livestock
precision livestock farming
transfer learning
Augmented reality
Dairies
Deep learning
Farms
Image segmentation
Losses
Dairy cow
Dairy cow identification
Deep learning
Estrus detection
Images processing
Livestock
Precision livestock farming
Region-of-interest
Regions of interest
Transfer learning
Mountings
animal
artificial intelligence
augmented reality
bovine
dairying
deep learning
estrus
female
livestock
milk
procedures
Animals
Artificial Intelligence
Augmented Reality
Cattle
Dairying
Deep Learning
Estrus Detection
Female
Livestock
Milk
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
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.
URI: https://doi.org/10.3390/s23249795
https://hdl.handle.net/11147/14259
ISSN: 1424-8220
Appears in Collections:PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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