Estimation of maize seedling number based on UAV multispectral data

doi:10.6046/zrzyyg.2021072

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Abstract

To monitor and evaluate maize seedlings in Northeast China and estimate their number in time, this study provided effective support for the rapid estimation of the maize seedling number using unmanned aerial vehicle (UAV) remote sensing images. Using the multispectral UAV data, the color indexes ExG, GBDI, ExG-ExR, NGRDI, and GLI were compared to segment maize seedlings from the soil background. Then, the optimal threshold was determined using the Otsu algorithm, and ExG was selected as the optimal color index. According to optimization, the best combination of morphological parameters consists of area (A), perimeter (B), rectangle length (D), rectangle perimeter (G), ellipse long axis length (H), and shape factor (Q). Then, the number of maize seedlings was predicted using the support vector regression (SVR) model and the prediction accuracy was assessed. Finally, the spatial distribution map of the local maize seedling number was developed. Tests revealed that the accuracy and the statistical error of the SVR model were 96.54% and 0.6%, respectively. These results allow the number and growth trends of maize seedlings to be predicted quickly and accurately in a short time.

Keywords UAV seedling number support vector regression (SVR) color index

ZTFLH:

TP79

Corresponding Authors: HUANG Yang E-mail: 614639191@qq.com;1875818447@qq.com

Issue Date: 14 March 2022

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	Xiaowei ZHAO
	Yang HUANG
	Yongqiang WANG
	Ding CHU

Cite this article:

Xiaowei ZHAO,Yang HUANG,Yongqiang WANG, et al. Estimation of maize seedling number based on UAV multispectral data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 106-114.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021072 OR https://www.gtzyyg.com/EN/Y2022/V34/I1/106

Fig.1 Study area and monitoring quadrat

Fig.2 Characteristic parameters of original UAV image

Fig.3 Weed removal

Fig.4 Data results of quadrat 23 under different indexes

Fig.5 Weed removal effect of different methods

Fig.6 Training and testing results of estimating number of seedling based on ExG index

Tab.1 Training and testing accuracy of each index

Fig.7 Pearson coefficient of the measured number of characteristic parameters

Tab.2 Combination of characteristic parameter

Fig.8 Comparative analysis of measured and predicted plant numbers

Tab.3 Overestimation and underestimation of measured and predicted plant numbers

Fig.9 Object recognition in different monitoring unit scales

Fig.10 Random rectangular plot and emergence rate

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