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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Albert, Paul, Saadeldin, Mohamed, Narayanan, Badri, Fernandez, Jaime B. orcid logoORCID: 0000-0001-9774-3879, Mac Namee, Brian, Hennessy, Deirdre orcid logoORCID: 0000-0002-7375-3754, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 (2022) Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation. In: Agriculture-Vision: 3rd International Workshop and Prize Challenge at CVPR 2022, 19 - 24 June 2022, New Orleans, USA. (In Press)

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automated dry herbage biomass monitoring www.github.com/PaulAlbert31/Clover_SSL
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Additional Information:In conjunction with IEEE/CVF CVPR 2022
Uncontrolled Keywords:Drone images; super resolution; vision for agriculture; grass clover; grass; clover; generative adversarial networks; GAN; regression;
Subjects:Computer Science > Artificial intelligence
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: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). . IEEE.
Official URL:https://doi.org/10.1109/CVPRW56347.2022.00170
Copyright Information:© 2022 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, SFI/16/RC/3835andSFI/12/RC/2289P2
ID Code:27025
Deposited On:21 Jun 2022 13:41 by Paul Albert . Last Modified 26 Jan 2024 15:39

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