YOLOv7 Computer Vision Project
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Here are a few use cases for this project:
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Poultry Farm Monitoring: Use YOLOv7 to automatically monitor and identify unhealthy or stressed birds within a poultry farm to ensure their well-being and overall farm productivity.
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Poultry Disease Management: Detect specific diseases like Diseased Eye, Pendulous Crop, or Slipped Tendon in chickens, enabling farm managers to provide timely treatments and reduce the risk of disease spread within the facility.
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Growth Tracking and Optimization: Track the weight of the birds (given in gm) in a poultry farm to optimize their growth and improve the planning of feeding regimes, informing future breeding and selection practices.
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Automated Poultry Sorting: Use the YOLOv7 model to automatically segregate broilers, hens, and chickens of varying sizes and ages into different groups, streamlining the workflow within the poultry farm.
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Poultry Industry Research: Analyze collected data from the model on various AI classes to inform research on poultry breeding programs, disease control methods, and stress reduction among poultry populations.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
yolov7-y40an_dataset,
title = { YOLOv7 Dataset },
type = { Open Source Dataset },
author = { YOLOv7 },
howpublished = { \url{ https://universe.roboflow.com/yolov7-dvav5/yolov7-y40an } },
url = { https://universe.roboflow.com/yolov7-dvav5/yolov7-y40an },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2024-11-21 },
}