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Selected Papers from the Eurasian Conference on Educational Innovation 2019
Open AccessArticle

Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters

by Umut Hasan 1,2, Mamat Sawut 1,2,3,* and Shuisen Chen 4
1
College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
2
Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830046, China
3
Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830046, China
4
Guangzhou Institute of Geography, Guangzhou 510070, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(23), 6829; https://doi.org/10.3390/su11236829
Received: 18 September 2019 / Revised: 19 November 2019 / Accepted: 20 November 2019 / Published: 2 December 2019
(This article belongs to the Section Sustainable Agriculture, Food and Wildlife)
The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management. View Full-Text
Keywords: leaf area index; jointing stage; UAV; grey correlation analysis; vegetation indices leaf area index; jointing stage; UAV; grey correlation analysis; vegetation indices
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Hasan, U.; Sawut, M.; Chen, S. Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters. Sustainability 2019, 11, 6829.

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