Broiler Chicken Computer Vision Project
We are a team of researchers from the University of Agricultural, Forest and Food Sciences HAFL in Switzerland, investigating techniques for emission mitigation in broiler houses. In February 2024, we conducted a case-control study, continuously measuring emissions over an entire production cycle in two identical broiler houses located in the eastern part of Switzerland. (one campain per season) One house operated with a heat exchanger, while the other used only a conventional gas-fueled heating system as a reference.
We measured concentrations of ammonia (NH3) and carbon dioxide (CO2) at all air inlets and outlets using XNode sensors. Additionally, ventilation rates were continuously monitored at each outlet using measuring fans. To accurately track the number of chickens inside each broiler house, we employed nine cameras per house to monitor the outside area and count the chickens. This dataset is used to train our object detection model to automatically count the chickens.
https://www.bfh.ch/en/research/research-areas/gaseous-emissions-agriculture/
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
broiler-chicken-hh3fw_dataset,
title = { Broiler Chicken Dataset },
type = { Open Source Dataset },
author = { GEL },
howpublished = { \url{ https://universe.roboflow.com/gel/broiler-chicken-hh3fw } },
url = { https://universe.roboflow.com/gel/broiler-chicken-hh3fw },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jun },
note = { visited on 2024-06-29 },
}
Connect Your Model With Program Logic
Find utilities and guides to help you start using the Broiler Chicken project in your project.
