File(s) under embargo

Reason: Publisher requirement

5

day(s)

until file(s) become available

Spectral analysis and mapping of blackgrass weed by leveraging machine learning and UAV multispectral imagery

journal contribution
posted on 14.12.2021, 11:57 authored by Jinya Su, Dewei Yi, Matthew CoombesMatthew Coombes, Cunjia LiuCunjia Liu, Xiaojun Zhai, Klaus McDonald-Maier, Wen-Hua ChenWen-Hua Chen
Accurate weed mapping is a prerequisite for site-specific weed management to enable sustainable agriculture. This work aims to analyse (spectrally) and mapping blackgrass weed in wheat fields by integrating Unmanned Aerial Vehicle (UAV), multispectral imagery and machine learning techniques. 18 widely-used Spectral Indices (SIs) are generated from 5 raw spectral bands. Then various feature selection algorithms are adopted to improve model simplicity and empirical interpretability. Random Forest classifier with Bayesian hyperparameter optimization is preferred as the classification algorithm. Image spatial information is also incorporated into the classification map by Guided Filter. The developed framework is illustrated with an experimentation case in a naturally blackgrass infected wheat field in Nottinghamshire, United Kingdom, where multispectral images were captured by RedEdge on-board DJI S-1000 at an altitude of 20m with a ground spatial resolution of 1.16 cm/pixel. Experimental results show that: (i) a good result (an average precision, recall and accuracy of 93.8%, 93.8%, 93.0%) is achieved by the developed system; (ii) the most discriminating SI is triangular greenness index (TGI) composed of Green-NIR, while wrapper feature selection can not only reduce feature number but also achieve a better result than using all 23 features; (iii) spatial information from Guided filter also helps improve the classification performance and reduce noises.

Funding

Enabling wide area persistent remote sensing for agriculture applications by developing and coordinating multiple heterogeneous platforms

Science and Technology Facilities Council

Find out more...

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

Science and Technology Facilities Council

Find out more...

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Computers and Electronics in Agriculture

Volume

192

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Crown. Published by Elsevier

Publisher statement

This paper was accepted for publication in the journal Computers and Electronics in Agriculture and the definitive published version is available at https://doi.org/10.1016/j.compag.2021.106621

Acceptance date

05/12/2021

Publication date

2021-12-14

Copyright date

2022

ISSN

0168-1699

Language

en

Depositor

Dr Cunjia Liu . Deposit date: 14 December 2021

Article number

106621