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Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery

文献类型： 外文期刊

作者： Zhang, Meina 1 ; Zhou, Jianfeng 2 ; Sudduth, Kenneth A. 3 ; Kitchen, Newell R. 3 ;

作者机构： 1.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Jiangsu, Peoples R China

2.Univ Missouri, Div Food Syst & Bioengn, Columbia, MO 65211 USA

3.USDA ARS, Cropping Syst & Water Qual Res Unit, Columbia, MO 65211 USA

关键词： Maize; UAV; Yield prediction; Colour feature; Modelling; Variable-rate application

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）

ISSN： 1537-5110

年卷期： 2020 年 189 卷

页码：

收录情况： SCI

摘要： Accurate crop yield estimation is important for agronomic and economic decision-making. This study evaluated the performance of imagery data acquired using a unmanned aerial vehicle (UAV)-based imaging system for estimating yield of maize (Zea mays L.) and the effects of variable-rate nitrogen (N) application on crops. Images of a 27-ha maize field were captured using a UAV with a consumer-grade RGB camera flying at similar to 100 m above ground level at three maize growth stages. The collected sequential images were stitched and the Excess Green (ExG) colour feature was extracted to develop prediction models for maize yield and to examine the effect of the variable-rate N application. Various linear regression models between ExG and maize yield were developed for three sample area sizes (21, 106, and 1058 m(2)). The model performance was evaluated using coefficient of determination (R-2), F-test and the mean absolute percentage error (MAPE) between estimated and actual yield. All linear regression models between ExG and yield were significant (p <= 0.05). The MAPE ranged from 6.2 to 15.1% at the three sample sizes, although R-2 values were all <0.5. Prediction error was lower at the later growth stages, as the crop approached maturity, and at the largest sample level. The ExG image feature showed potential for evaluating the effect of variable-rate N application on crop growth. Overall, the low-cost UAV imaging system provided useful information for field management. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.

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