Loughborough University
Browse
s11119-023-09999-3 (2).pdf (3.75 MB)

Generalized data-driven optimal path planning framework for uniform coverage missions using crop spraying UAVs

Download (3.75 MB)
journal contribution
posted on 2023-06-30, 14:55 authored by Rohit NanavatiRohit Nanavati, Yanhua Meng, Matthew CoombesMatthew Coombes, Cunjia LiuCunjia Liu

Unmanned aerial vehicle (UAV) based crop spraying has become a popular alternative in the field of precision agriculture. One of the key goals of UAV based spraying is achieving spray coverage that is as uniform as possible to ensure maximum spray efficacy. Most of the existing studies in the literature focus on analysing the effects of spraying parameters on the uniformity of coverage distribution using experimental studies. However, in this work, we propose a novel generalized data-driven optimal path-planning framework aimed at finding the optimal operational flight parameters (flight speed and pass widths) for a lawnmower coverage path plan to meet the specified spray coverage rate while ensuring the uniformity. The framework takes a spray distribution model as an input and computes the optimal operational parameters for the coverage path plan to minimize coverage non-uniformity without making any assumptions on the UAV type. Furthermore, we also propose a neural network structure using Gaussian kernel neurons to design the spraying model using experimental data. The neural network structure makes no assumption about the type of UAV, onboard nozzle placement, or the flight parameters. The accuracy of the modelling solution only depends on the quality of the training data. In other words, higher diversity of the training data is in terms of the flight and spraying parameters would result in a modelling solution that is more representative of the spraying distribution and consequently improve the quality of the operational parameters obtained from the proposed optimization framework. In this work, we present a case study to demonstrate the use case and test the performance of the proposed framework simulation and experiments using the DJI AGRAS-T10 drone. The results showed that the optimal pass-width solutions for low forward speeds were similar to optimizing the positioning of the nozzles on a boom sprayer to achieve uniform coverage. Whereas, at high speeds, the passwidth was comparatively higher as the spread of the effective coverage over each pass increased. A discussion contextualized in the case study is provided to highlight the salient features and limitations of the proposed framework.

Funding

Space-enabled Crop disEase maNagement sErvice via Crop sprAying Drones (SCENE-CAD)

Science and Technology Facilities Council

Find out more...

Cooperative data analysing for citrus greening monitoring based on UAV remote sensing: Royal Society grant no. IEC\NSFC\191320

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Precision Agriculture

Volume

24

Issue

4

Pages

1497 - 1525

Publisher

Springer

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Acceptance date

2023-02-19

Publication date

2023-03-16

Copyright date

2023

ISSN

1385-2256

eISSN

1573-1618

Language

  • en

Depositor

Dr Rohit Nanavati. Deposit date: 13 March 2023

Usage metrics

    Loughborough Publications

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC