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Rogers, Harry, 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
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.
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