Wheat Biomass Estimation in Different Growth Stages Based on Color and Texture Features of UAV Images

doi:10.12133/j.smartag.SA202202004

Abstract

Abstract:

In order to realize the rapid and non-destructive monitoring of wheat biomass, field wheat trials were conducted based on different densities, nitrogen fertilizers and varieties, and unmanned aerial vehicle (UAV) was used to obtain RGB images in the pre-wintering stage, jointing stage, booting stage and flowering stage of wheat. The color and texture feature indices of wheat were obtained using image processing, and wheat biomass was obtained by manual field sampling in the same period. Then the relationship between different color and texture feature indices and wheat biomass was analyzed to select the suitable feature index for wheat biomass estimation. The results showed that there was a high correlation between image color index and wheat biomass in different stages, the values of r were between 0.463 and 0.911 (P<0.05). However, the correlation between image texture feature index and wheat biomass was poor, only 5 index values reached significant or extremely significant correlation level. Based on the above results, the color indices with the highest correlation to wheat biomass or the combining indices of color and texture features in different growth stages were used to construct estimation model of wheat biomass. The models were validated using independently measured biomass data, and the correlation between simulated and measured values reached the extremely significant level (P<0.01), and root mean square error (RMSE) was smaller. The R² of color index model in the four stages were 0.538, 0.631, 0.708 and 0.464, and RMSE were 27.88, 516.99, 868.26 and 1539.81 kg/ha, respectively. The R² of the model combined with color and texture index were 0.571, 0.658, 0.753 and 0.515, and RMSE were 25.49, 443.20, 816.25 and 1396.97 kg/ha, respectively. This indicated that the estimated results using the models were reliable and accurate. It also showed that the estimation models of wheat biomass combined with color and texture feature indices of UAV images were better than the single color index models.

Key words: wheat, UAV?image, color?index, texture?feature?index, biomass, texture index

CLC Number:

S512

DAI Mian, YANG Tianle, YAO Zhaosheng, LIU Tao, SUN Chengming. Wheat Biomass Estimation in Different Growth Stages Based on Color and Texture Features of UAV Images[J]. Smart Agriculture, 2022, 4(1): 71-83.

Figures/Tables 6

Table 1

Algorithms of color indices

Index	Abbreviation	Computational formula
Visible Atmospheric Resistance Vegetation Index	VARI	$V A R I = g - r / g + r$ （1）
Excess Red Vegetation Index	ExR	ExR= $1.4 r - g$ （2）
Excess Green Vegetation Index	ExG	ExG= $2 g - r - b$ （3）
Green Leaf Vegetation Index	GLI	GLI= $2 g - b - r / 2 g + b + r$ （4）
Excess Green-Red Difference Index	ExGR	ExGR =ExG $-$ ExR （5）
Normalized Difference Index	NDI	NDI= $g - r / g + r$ （6）
Modified Green Red Vegetation Index	MGRVI	MGRVI= $g 2 - r 2 / g 2 + r 2$ （7）
Red Green Blue Vegetation Index	RGBVI	RGBVI= $g 2 - b r / g 2 + b r$ （8）

Table 1

Table 2

Algorithms of texture feature indices

Index	Abbreviation	Computational formula
Angular Second Moment	ASM	$A S M = ∑ i ∑ j P (i, j) 2$ （9）
Contrast	CON	$C O N = ∑ i ∑ j i - j 2 P (i, j)$ （10）
Correlation	COR	COR= $∑ i ∑ j i, j P i, j - ??????????????? μ x μ y / σ x σ y$ （11）
Entropy	ENT	ENT= $- ∑ i ∑ j P i, j l o g ? P i, j$ （12）

Table 2

Table 3

Table 4

Fig. 1

Fig. 2

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Growth stage	ExG	VDI	ExR	ExGR	VARI	GLI	MGRVI	RGBVI
Pre-wintering stage	0.594**	0.458*	-0.619**	0.678**	0.743**	0.657**	0.706**	0.598**
Jointing stage	0.813**	0.823**	-0.824**	0.911**	0.817**	0.809**	0.687**	0.625**
Booting stage	0.493*	0.793**	-0.779**	0.607**	0.734**	0.483*	0.817**	0.351
Flowering stage	0.367	0.540**	-0.652**	0.463*	0.679**	0.369	0.540**	0.316

Growth stage	ASM	CON	COR	ENT
Pre-wintering stage	0.573**	0.195	-0.132	-0.564**
Jointing stage	0.271	0.511**	-0.574**	-0.072
Booting stage	-0.200	0.417*	-0.351	0.260
Flowering stage	-0.222	0.123	-0.167	0.126

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