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Rogers, Harry 
ORCID: https://orcid.org/0000-0003-3227-5677 and Zebin, Tahmina ORCID: https://orcid.org/0000-0003-0437-0570
(2022)
Explainable Droplet Recognition System for Precision Sprayer Applications.
In: FARSCOPE CDT Conference, 2022-07-11 - 2022-07-15, Bristol.
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Abstract
AI-driven detection systems are playing an increasingly important role in the advancement of precision agriculture. In this paper, we have implemented a transfer learning pipeline for water droplet detection with the intent to develop quantifiable and real-time detection of post-spray areas for precision spraying applications. The object detection pipeline effectively identified multiple features for water droplet detection from the three curated datasets. We have used two pre-trained convolutional backbones as the feature extractor and achieved an overall detection mean average precision across the three curated datasets of 0.409 and 0.277 for the ResNet50, and MobileNetV3-Large backbones respectively. Additionally, for visual explanations and interpretation, we implemented EigenCAM class activation mapping techniques to highlight the regions of the input images that are important for predictions.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | explainable ai,precision spraying,computer vision,sdg 9 - industry, innovation, and infrastructure ,/dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies |
| Depositing User: | LivePure Connector |
| Date Deposited: | 03 Aug 2022 08:30 |
| Last Modified: | 06 May 2024 00:08 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/87062 |
| DOI: |
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