Albert, Paul, Saadeldin, Mohamed, Narayanan, Badri, Mac Namee, Brian ORCID: 0000-0003-2518-0274, Hennessy, Deirdre
ORCID: 0000-0002-7375-3754, O'Connor, Aisling, O'Connor, Noel E.
ORCID: 0000-0002-4033-9135 and McGuinness, Kevin
ORCID: 0000-0003-1336-6477
(2021)
Semi-supervised dry herbage mass estimation using automatic data and
synthetic images.
In: Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA), 11 Oct 2021, Virtual.
Abstract
Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate species-specific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study
low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic pro-
cess to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a
herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Workshop |
Refereed: | Yes |
Additional Information: | Workshop held in conjunction with ICCV 2021, the International Conference on Computer Vision, October 11- October 17, 2021. |
Uncontrolled Keywords: | Computer Vision; Agriculture; Plant phenotyping; GrassClover; biomass prediction; herbage mass prediction |
Subjects: | Computer Science > Image processing Computer Science > Machine learning |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering Research Institutes and Centres > INSIGHT Centre for Data Analytics |
Published in: | 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). . IEEE Computer Society. |
Publisher: | IEEE Computer Society |
Official URL: | https://dx.doi.org/10.1109/ICCVW54120.2021.00149 |
Copyright Information: | © 2021 The Authors |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Science Foundation Ireland (SFI) under grant number SFI/15/SIRG/3283 and SFI/12/RC/2289 References P2. |
ID Code: | 26424 |
Deposited On: | 03 Nov 2021 11:34 by Paul Albert . Last Modified 25 Apr 2022 12:51 |
Documents
Full text available as:
Preview |
PDF
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
12MB |
Downloads
Downloads
Downloads per month over past year
Archive Staff Only: edit this record