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Article

Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

by 1,2, 1, 1 and 1,2,*
1
The State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences, Nanjing 210008, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3228; https://doi.org/10.3390/rs12193228
Received: 23 August 2020 / Revised: 2 October 2020 / Accepted: 2 October 2020 / Published: 3 October 2020
(This article belongs to the Special Issue Remote and Proximal Assessment of Plant Traits)
The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production. View Full-Text
Keywords: rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy rice; growth indicators; multi-stage vegetation index; unmanned aerial vehicle; optimal index method; object-oriented segmentation method; estimation accuracy
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MDPI and ACS Style

Qiu, Z.; Xiang, H.; Ma, F.; Du, C. Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery. Remote Sens. 2020, 12, 3228. https://doi.org/10.3390/rs12193228

AMA Style

Qiu Z, Xiang H, Ma F, Du C. Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery. Remote Sensing. 2020; 12(19):3228. https://doi.org/10.3390/rs12193228

Chicago/Turabian Style

Qiu, Zhengchao, Haitao Xiang, Fei Ma, and Changwen Du. 2020. "Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery" Remote Sensing 12, no. 19: 3228. https://doi.org/10.3390/rs12193228

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