End-to-End Deep Learning Models for Gap Identification in Maize Fields
Abstract
We propose an approach to jointly count plants and detect gaps in maize fields using end-to-end deep-learning models. Unlike previous efforts that focused solely on plant detection, our methodology also integrates the task of gap identification, offering a holistic view of the state of the agricultural field. Moreover, we consider different data sources in our experiments and explore the benefits of using multispectral over RGB images, which are commonly used in the industry. The findings suggest that multi-task learning on multispectral images significantly outperforms other model configurations, demonstrating the potential of the proposed approach for precision agriculture.
Cite
Text
Waqar et al. "End-to-End Deep Learning Models for Gap Identification in Maize Fields." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00549Markdown
[Waqar et al. "End-to-End Deep Learning Models for Gap Identification in Maize Fields." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/waqar2024cvprw-endtoend/) doi:10.1109/CVPRW63382.2024.00549BibTeX
@inproceedings{waqar2024cvprw-endtoend,
title = {{End-to-End Deep Learning Models for Gap Identification in Maize Fields}},
author = {Waqar, Rana and Grbovic, Zeljana and Khan, Maryam and Pajevic, Nina and Stefanovic, Dimitrije and Filipovic, Vladan and Panic, Marko and Djuric, Nemanja},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {5403-5412},
doi = {10.1109/CVPRW63382.2024.00549},
url = {https://mlanthology.org/cvprw/2024/waqar2024cvprw-endtoend/}
}