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Rogers, Harry 
ORCID: https://orcid.org/0000-0003-3227-5677, de la Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826, Zebin, Tahmina, Cielniak, Grzegorz and Magri, Ben
(2024)
Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation.
Neural Computing & Applications, 36 (32).
20211–20229.
ISSN 0941-0643
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Abstract
Modern agriculture relies heavily on the precise application of chemicals such as fertilisers, herbicides, and pesticides, which directly affect both crop yield and environmental footprint. Therefore, it is crucial to assess the accuracy of precision sprayers regarding the spatial location of spray deposits. However, there is currently no fully automated evaluation method for this. In this study, we collected a novel dataset from a precision spot spraying system to enable us to classify and detect spray deposits on target weeds and non-target crops. We employed multiple deep convolutional backbones for this task; subsequently, we have proposed a robustness testing methodology for evaluation purposes. We experimented with two novel data augmentation techniques: subtraction and thresholding which enhanced the classification accuracy and robustness of the developed models. On average, across nine different tests and four distinct convolutional neural networks, subtraction improves robustness by 50.83%, and thresholding increases by 42.26% from a baseline. Additionally, we have presented the results from a novel weakly supervised object detection task using our dataset, establishing a baseline Intersection over Union score of 42.78%. Our proposed pipeline includes an explainable artificial intelligence stage and provides insights not only into the spatial location of the spray deposits but also into the specific filtering methods within that spatial location utilised for classification.
| Item Type: | Article |
|---|---|
| Additional Information: | Data availability statement: The data currently are unavailable. Reason for Unavailability: We would like to refine the dataset and then open source in future. The data were also collected on a proprietary system. Funding information: This work is supported by the Engineering and Physical Sciences Research Council [EP/S023917/1]. This work is also supported by Syngenta as the Industrial partner. The research presented in this paper was carried out on the High-Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia. |
| Uncontrolled Keywords: | agri-robotics,computer vision,data augmentation,xai,software,artificial intelligence,sdg 12 - responsible consumption and production ,/dk/atira/pure/subjectarea/asjc/1700/1712 |
| Faculty \ School: | Faculty of Science Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 14 Aug 2024 13:30 |
| Last Modified: | 19 Nov 2024 01:31 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96250 |
| DOI: | 10.1007/s00521-024-10142-0 |
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