Detecting Wheat Powdery Mildew and Predicting Grain Yield Using Unmanned Aerial Photography

Liu, W., Cao, X., Fan, J, Wang, Z., Yan, Z., Luo, Y., West, JonORCID logo, Xu, X. and Zhou, Y. (2018) Detecting Wheat Powdery Mildew and Predicting Grain Yield Using Unmanned Aerial Photography. Plant Disease, 102. pp. 1981-1988. 10.1094/PDIS-12-17-1893-RE
Copy

High-resolution aerial imaging with an unmanned aerial vehicle (UAV) was used to quantify wheat powdery mildew and estimate grain yield. Aerial digital images were acquired at Feekes growth stage (GS) 10.5.4 from flight altitudes of 200 m, 300 m and 400 m during the 2009-2010 and 2010-2011 seasons; and 50 m, 100 m, 200 m and 300 m during the 2011-2012, 2012-2013 and 2013-2014 seasons. The image parameter lgR was consistently correlated positively with wheat powdery mildew severity and wheat grain yield for all combinations of flight altitude and year. Fitting the data with random coefficient regression models showed that the exact relationship of lgR with disease severity and grain yield varied considerably from year to year and to a lesser extent with flight altitude within the same year. The present results raise an important question about the consistency of using remote imaging information to estimate disease severity and grain yield. Further research is needed to understand the nature of inter-year variability in the relationship of remote imaging data with disease or grain yield. Only then can we determine how the remote imaging tool can be used in commercial agriculture.


picture_as_pdf
Wei Liu 2018 Remote sensing for yield and wheat PM pdis-12-17-1893-re.pdf
subject
Published Version
Available under Creative Commons: Attribution 4.0

View Download

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads