An Automated Precision Spraying Evaluation System

Rogers, Harry ORCID: https://orcid.org/0000-0003-3227-5677, Iglesia, Beatriz De La ORCID: https://orcid.org/0000-0003-2675-5826, Zebin, Tahmina ORCID: https://orcid.org/0000-0003-0437-0570, Cielniak, Grzegorz and Magri, Ben (2023) An Automated Precision Spraying Evaluation System. In: Towards Autonomous Robotic Systems - 24th Annual Conference, TAROS 2023, Proceedings. Springer, pp. 26-37. ISBN 978-3-031-43359-7

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Abstract

Data-driven robotic systems are imperative in precision agriculture. Currently, Agri-Robot precision sprayers lack automated methods to assess the efficacy of their spraying. In this paper, images were collected from an RGB camera mounted to an Agri-robot system to locate spray deposits on target weeds or non-target lettuces. We propose an explainable deep learning pipeline to classify and localise spray deposits without using existing manual agricultural methods. We implement a novel stratification and sampling methodology to improve classification results. Spray deposits are identified with over 90% Area Under the Receiver Operating Characteristic and over 50% Intersection over Union for a Weakly Supervised Object Localisation task. This approach utilises near real-time architectures and methods to achieve inference for both classification and localisation in 0.062 s on average.

Item Type: Book Section
Additional Information: This work is supported by the UK Engineering and Physical Sciences Research Council [EP/S023917/1]. This work is also supported by Syngenta as the Industrial partner.
Uncontrolled Keywords: agri-robotics,computer vision,xai,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School: Faculty of Science > School of Computing Sciences
UEA Research Groups: Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging
Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023)
Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Faculty of Science > Research Groups > Data Science and Statistics
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Depositing User: LivePure Connector
Date Deposited: 07 Oct 2023 01:31
Last Modified: 28 Apr 2024 06:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/93191
DOI: 10.1007/978-3-031-43360-3_3

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