Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review

Yang, Xiao; Bahadur Bist, Ramesh; Paneru, Bidur; Liu, Tianming; Applegate, Todd; Ritz, Casey; Kim, Woo; Regmi, Prafulla; Chai, Lilong

doi:10.1016/j.compag.2024.109339

Computers and Electronics in Agriculture

2024

DOI: 10.1016/j.compag.2024.109339

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Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review

Xiao Yang,

Ramesh Bahadur Bist,

Bidur Paneru

et al.

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Cited by 3 publications

References 120 publications

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Breaking the barriers of technology adoption: Explainable AI for requirement analysis and technology design in smart farming

Mallinger,

Corpaci,

Neubauer

et al. 2024

Smart Agricultural Technology

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Breaking the barriers of technology adoption: Explainable AI for requirement analysis and technology design in smart farming

Mallinger,

Corpaci,

Neubauer

et al. 2024

Smart Agricultural Technology

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Sustainable poultry farming practices: a critical review of current strategies and future prospects

Bist,

Poudel

et al. 2024

Poultry Science

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A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza

Yang,

Bist,

Subedi

et al. 2024

AgriEngineering

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The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 (mAP@0.5) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds.

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Computer Vision-Based cybernetics systems for promoting modern poultry Farming: A critical review

Cited by 3 publications

References 120 publications

Breaking the barriers of technology adoption: Explainable AI for requirement analysis and technology design in smart farming

Breaking the barriers of technology adoption: Explainable AI for requirement analysis and technology design in smart farming

Sustainable poultry farming practices: a critical review of current strategies and future prospects

A Machine Vision System for Monitoring Wild Birds on Poultry Farms to Prevent Avian Influenza

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