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Wheat drought assessment by remote sensing imagery using unmanned aerial vehicle

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conference contribution
posted on 07.06.2019 by Jinya Su, Matthew Coombes, Cunjia Liu, Lei Guo, Wen-Hua Chen
This work aims at evaluating the usability of remote sensing RGB imagery by an Unmanned Aerial Vehicle (UAV) in assessing wheat drought status. A UAV survey is conducted to collect high-resolution RGB imageries by using DJI S1000 for the experimental wheat fields of Gucheng town, Heibei Province, China. The soil moisture for different plots of the experimental filed is kept at an approximately constant level for the whole growing season in a well controlled environment, where field measurements are performed just after the UAV survey to obtain the soil water content for each plot. A machine learning based wheat drought assessment framework is proposed in this work. In the proposed framework, wheat pixels are first segmented from the soil background using the classical normalized excess green index (NExG). Rather than using pixel-wise classification, a pixel square of appropriate dimension is defined as the samples, based on which various features are extracted for the wheat pixels including statistical features and spectral index features. Different classification algorithms are experimented to identify a suitable one in terms of classification accuracy and computation time. It is discovered that Support Vector Machine with Gaussian kernel can obtain an accuracy over 90%, which demonstrates the usefulness of RGB imagery in wheat drought assessment.

Funding

This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with grant number ST/N006852/1 and the Newton Network+ NeWMap project.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

2018 37th Chinese Control Conference (CCC) 2018 37th Chinese Control Conference (CCC)

Citation

SU, J. .... et al., 2018. Wheat drought assessment by remote sensing imagery using unmanned aerial vehicle. Presented at the 2018 37th Chinese Control Conference (CCC), Wuhan, China, 25-27 July 2018.

Publisher

IEEE

Version

AM (Accepted Manuscript)

Acceptance date

21/05/2018

Publication date

2018

Notes

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9789881563958

Language

en

Location

Wuhan, China

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