An Automated Precision Spraying Evaluation System

Harry Rogers, Beatriz De La Iglesia, Tahmina Zebin, Grzegorz Cielniak, Ben Magri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems - 24th Annual Conference, TAROS 2023, Proceedings
EditorsFumiya Iida, Perla Maiolino, Arsen Abdulali, Mingfeng Wang
PublisherSpringer
Pages26-37
Number of pages12
ISBN (Electronic)978-3-031-43360-3
ISBN (Print)978-3-031-43359-7
DOIs
Publication statusPublished - 8 Sep 2023

Keywords

  • Agri-Robotics
  • Computer Vision
  • XAI

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