ArticleAnimals : an open access journal from MDPI2024
Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modernizing Livestock Operations: Smart Feedlot Technologies and Their Impact.Animals : an open access journal from MDPI · 2026Review
- Multi-Object Detection of Forage Density and Dairy Cow Feeding Behavior Based on an Improved YOLOv10 Model for Smart Pasture Applications.Sensors (Basel, Switzerland) · 2026Article
- Automated Cattle Head and Ear Pose Estimation Using Deep Learning for Animal Welfare Research.Veterinary sciences · 2025Article
- YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation.Sensors (Basel, Switzerland) · 2025Article
- Exploring the impact of hyperparameter and data augmentation in YOLO V10 for accurate bone fracture detection from X-ray images.Scientific reports · 2025Article
- Body Temperature Detection of Group-Housed Pigs Based on the Pairing of Left and Right Ear Roots in Thermal Images.Animals : an open access journal from MDPI · 2025Article
- YOLOv8-CBAM: a study of sheep head identification in Ujumqin sheep.Frontiers in veterinary science · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
This study highlights the importance of monitoring cattle feeding behavior using the YOLO algorithm for object detection. Videos of six Charolais bulls were recorded on a French farm, and three feeding behaviors (biting, chewing, visiting) were identified and labeled using Roboflow. YOLOv8 and YOLOv10 were compared for their performance in detecting these behaviors. YOLOv10 outperformed YOLOv8 with slightly higher precision, recall, mAP50, and mAP50-95 scores. Although both algorithms demonstrated similar overall accuracy (around 90%), YOLOv8 reached optimal training faster and exhibited less overfitting. Confusion matrices indicated similar patterns of prediction errors for both versions, but YOLOv10 showed better consistency. This study concludes that while both YOLOv8 and YOLOv10 are effective in detecting cattle feeding behaviors, YOLOv10 exhibited superior average performance, learning rate, and speed, making it more suitable for practical field applications.
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Registered trials
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