Wheat Drought Assessment by Remote Sensing Imagery Using Unmanned Aerial Vehicle

Jinya Su, Matthew Coombes, Cunjia Liu, Lei Guo, Wen Hua Chen

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages10340-10344
Number of pages5
ISBN (Electronic)9789881563941
DOIs
Publication statusPublished - 5 Oct 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Classification
  • Remote sensing
  • UAV imagery
  • Wheat drought

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