An Agricultural Precision Sprayer Deposit Identification System

Rogers, Harry ORCID: https://orcid.org/0000-0003-3227-5677, De La Iglesia, Beatriz 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 Agricultural Precision Sprayer Deposit Identification System. In: 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). IEEE International Conference on Automation Science and Engineering . UNSPECIFIED, NZL. ISBN 9798350320695

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Abstract

Data-driven Artificial Intelligence systems are playing an increasingly significant role in the advancement of precision agriculture. Currently, precision sprayers lack fully automated methods to evaluate effectiveness of their operation, e.g. whether spray has landed on target weeds. In this paper, using an agricultural spot spraying system images were collected from an RGB camera to locate spray deposits on weeds or lettuces. We present an interpretable deep learning pipeline to identify spray deposits on lettuces and weeds without using existing methods such as tracers or water sensitive papers. We implement a novel stratification and sampling methodology to improve results from a baseline. Using a binary classification head after transfer learning networks, spray deposits are identified with over 90% Area Under the Receiver Operating Characteristic (AUROC). This work offers a data-driven approach for an automated evaluation methodology for the effectiveness of precision sprayers.

Item Type:	Book Section
Additional Information:	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.
Uncontrolled Keywords:	control and systems engineering,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2207
Faculty \ School:	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 Statistics Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023)
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	07 Oct 2023 01:31
Last Modified:	24 Apr 2024 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93190
DOI:	10.1109/case56687.2023.10260374

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