A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza DOI Creative Commons
Xiao Yang, Ramesh Bahadur Bist, Sachin Subedi

et al.

AgriEngineering, Journal Year: 2024, Volume and Issue: 6(4), P. 3704 - 3718

Published: Oct. 9, 2024

The epidemic of avian influenza outbreaks, especially high-pathogenicity (HPAI), which causes respiratory disease and death, is a disaster in poultry. outbreak HPAI 2014–2015 caused the loss 60 million chickens turkeys. most recent outbreak, ongoing since 2021, has led to over 50 so far US Canada. Farm biosecurity management practices have been used prevent spread virus. However, existing related controlling transmission virus through wild birds, waterfowl, are limited. For instance, ducks were considered hosts viruses many past outbreaks. objectives this study develop machine vision framework for tracking birds test performance deep learning models detection on poultry farms. A based computer was designed applied monitoring birds. night camera collect data bird near In data, there two main birds: gadwall brown thrasher. More than 6000 pictures extracted random video selection training testing processes. An overall precision 0.95 ([email protected]) reached by model. model capable automatic real-time Missed mainly came from occlusion because tended hide grass. Future research could be focused applying alert risk combining it with unmanned aerial vehicles drive out detected

Language: Английский

Sustainable poultry farming practices: a critical review of current strategies and future prospects DOI Creative Commons
Ramesh Bahadur Bist,

K. S. Bist,

Sandesh Poudel

et al.

Poultry Science, Journal Year: 2024, Volume and Issue: 103(12), P. 104295 - 104295

Published: Sept. 4, 2024

Language: Английский

Citations

29
Smart glasses in the chicken barn: Enhancing animal welfare through mixed reality DOI
Dorian Baltzer,

Shannon Douglas,

Jan‐Henrik Haunert

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: 10, P. 100786 - 100786

Published: Jan. 18, 2025

Language: Английский

Citations

1
Breaking the Barriers of Technology Adoption: Explainable AI for Requirement Analysis and Technology Design in Smart Farming DOI Creative Commons
Kevin Mallinger, Luiza Corpaci, Thomas Neubauer

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100658 - 100658

Published: Nov. 1, 2024

Language: Английский

Citations

4
Automatic monitoring of activity intensity in a chicken flock using a computer vision-based background image subtraction technique: an experimental infection study with fowl adenovirus DOI Creative Commons
Hiroshi Iseki, Eri Furukawa, Toshiaki Shimasaki

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100821 - 100821

Published: Feb. 1, 2025

Language: Английский

Citations

0
YOLO-SDD: An Effective Single-Class Detection Method for Dense Livestock Production DOI Creative Commons
Yubin Guo, Zhipeng Wu, B. H. You

et al.

Animals, Journal Year: 2025, Volume and Issue: 15(9), P. 1205 - 1205

Published: April 23, 2025

Single-class object detection, which focuses on identifying, counting, and tracking a specific animal species, plays vital role in optimizing farm operations. However, dense occlusion among individuals group activity scenarios remains major challenge. To address this, we propose YOLO-SDD, detection network designed for single-class densely populated scenarios. First, introduce Wavelet-Enhanced Convolution (WEConv) to improve feature extraction under occlusion. Following an perception attention mechanism (OPAM), further enhances the model’s ability recognize occluded targets by simultaneously leveraging low-level detailed features high-level semantic features, helping model better handle Lastly, Lightweight Shared Head (LS Head) is incorporated specifically optimized tasks, enhancing efficiency while maintaining high accuracy. Experimental results ChickenFlow dataset, developed broiler show that n, s, m variants of YOLO-SDD achieve AP50:95 improvements 2.18%, 2.13%, 1.62% over YOLOv8n, YOLOv8s, YOLOv8m, respectively. In addition, our surpasses performance latest real-time detector, YOLOv11. also achieves state-of-the-art publicly available GooseDetect SheepCounter datasets, confirming its superior capability crowded livestock settings. YOLO-SDD’s enables automated counting conditions, providing robust solution precision farming.

Language: Английский

Citations

0
Progress and Trends of Non-contact Detection Methods for Poultry Growth Information: a review DOI Creative Commons
Xin He, Hao Xue,

Yuchen Jia

et al.

Poultry Science, Journal Year: 2025, Volume and Issue: unknown, P. 105281 - 105281

Published: May 1, 2025

Language: Английский

Citations

0
Cough sound recognition in poultry using portable microphones for precision medication guidance DOI
Hao Zhou, Qibing Zhu, Tomás Norton

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 237, P. 110541 - 110541

Published: May 15, 2025

Language: Английский

Citations

0
Enhancing Poultry Multi-Behavior Detection with Semi-Supervised Auto-Labeling and Prompt-Driven Zero-Shot Recognition DOI
Ramesh Bahadur Bist, Lilong Chai, Sachin Subedi

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza DOI Creative Commons
Xiao Yang, Ramesh Bahadur Bist, Sachin Subedi

et al.

AgriEngineering, Journal Year: 2024, Volume and Issue: 6(4), P. 3704 - 3718

Published: Oct. 9, 2024

The epidemic of avian influenza outbreaks, especially high-pathogenicity (HPAI), which causes respiratory disease and death, is a disaster in poultry. outbreak HPAI 2014–2015 caused the loss 60 million chickens turkeys. most recent outbreak, ongoing since 2021, has led to over 50 so far US Canada. Farm biosecurity management practices have been used prevent spread virus. However, existing related controlling transmission virus through wild birds, waterfowl, are limited. For instance, ducks were considered hosts viruses many past outbreaks. objectives this study develop machine vision framework for tracking birds test performance deep learning models detection on poultry farms. A based computer was designed applied monitoring birds. night camera collect data bird near In data, there two main birds: gadwall brown thrasher. More than 6000 pictures extracted random video selection training testing processes. An overall precision 0.95 ([email protected]) reached by model. model capable automatic real-time Missed mainly came from occlusion because tended hide grass. Future research could be focused applying alert risk combining it with unmanned aerial vehicles drive out detected

Language: Английский

Citations

0