基于無人機圖像顏色與紋理特征的小麥不同生育時期生物量估算

戴冕 楊天樂 姚照勝 劉濤 孫成明

摘要：為實現小麥生物量田間快速無損監測，開展基于不同密度、氮肥和品種處理的田間試驗，應用無人機獲取小麥越冬前期、拔節期、孕穗期和開花期4個時期的 RGB 圖像，通過影像處理獲取小麥顏色指數和紋理特征參數，并同時期通過田間取樣獲取小麥生物量;分析不同顏色指數和紋理特征參數與小麥生物量的關系，篩選出適合小麥生物量估算的顏色和紋理特征指數。結果表明，不同時期圖像顏色指數和小麥生物量均有較高的相關性，且大部分達到極顯著相關水平;圖像紋理特征指數與小麥生物量的相關性較差，只有少數指標達到顯著或極顯著相關水平?；谏鲜鼋Y果，研究利用相關性最高的顏色指數或顏色指數與紋理特征指數結合構建小麥不同生育時期的生物量估算模型，并通過獨立的實測生物量數據對模型進行了驗證，模型模擬值與實測值之間的相關性均達到了極顯著水平（ P<0.01）， RMSE 均較小。其中，顏色指數模型在4個時期的 R2分別為0.538、0.631、0.708和0.464，RMSE 分別為27.88、516.99、868.26和1539.81 kg/ha。而顏色和紋理指數結合的模型在4個時期的 R2分別為0.571、0.658、0.753和0.515， RMSE 分別為25.49、443.20、816.25和1396.97 kg/ha ，說明模型估算的結果是可靠的，且精度較高。同時結合無人機圖像顏色和紋理特征指數的小麥生物量估測模型的效果要優于單一顏色指數模型。研究可為小麥田間長勢實時監測與生物量估算提供新的手段。

關鍵詞：小麥;無人機圖像;顏色指數;紋理特征指數;生物量;紋理指數

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

DAI Mian1，2 ， YANG Tianle1，2 ， YAO Zhaosheng1，2 ， LIU Tao1，2 ， SUN Chengming1，2*

（1. Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation andPhysiology， Agricultural College， Yangzhou University， Yangzhou 225009， China;2. Jiangsu Co-Innovation Centerfor Modern Production Technology of Grain Crops， Yangzhou University， Yangzhou 225009， China ）

Abstract： In order to realize the rapid and non-destructive monitoring of wheat biomass， field wheat trials were con‐ ducted 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 col‐ or 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 val‐ues 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 mod‐els 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 com ‐bined 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???????????????????? Documents code：A????????????????? Article ID：SA202202004

Citation：DAI Mian， YANG Tianle， YAO Zhaosheng， LIU Tao， SUN Chengming. Wheat biomass estimation in dif‐ferent growth stages based on color and texture features of UAV images[J]. Smart Agriculture， 2022， 4（1）：71-83.（in English with Chinese abstract）

戴冕， 楊天樂， 姚照勝， 劉濤， 孫成明.基于無人機圖像顏色與紋理特征的小麥不同生育時期生物量估算[J].智慧農業（中英文）， 2022， 4（1）：71-83.

1? Introduction

Biomass is an important physical and chemical parameter in ecosystems and a significant index for assessing? the? life? activities? of vegetation? and? for monitoring growth and estimating crop yield[1]. Tra‐ditional? biomass? estimation? methods? are? not? only time-consuming? and? labor-intensive，? but? also? can not conduct large-scale monitoring[2]. The rapid de‐velopment? of remote? sensing technology? in recent years? has? accelerated? its? wide? application? in? crop biomass estimation， as it is fast， accurate， and non- destructive[1].

Based? on? the? spectral? features? of vegetation， previous studies have obtained many achievements in using vegetation indices to estimate biomass. Ji‐menez-Sierra? et? al.[3]? proposed? GBF-Sm-Bs? ap ‐proach by obtaining biomass estimation correlation of 0.995 with R2 =0.991. This result increased the precision? in? the? biomass? estimation? by? about 62.43% compared? to? previous? work. Hou? et? al.[4] built a biomass estimation model based on multiple vegetation indices， of which the index model of red edge location was used to estimate the wheat bio ‐ mass model， R2 were all greater than 0.8. Liu et al.[5] used arbor as an object to analyze the correlation be‐ tween biomass and biomass factors， and constructed a highly? accurate? forest biomass estimation model using the partial least-squares method， the correla‐tion coefficient between the predicted value and the measured? value? was 0.718，? and? the? accuracy? was 90.1%. However， the vegetation index is not sensi‐tive to canopy biomass changes in high-density sce‐narios and deviates easily when inverting physical and chemical parameters or even agronomic param ‐eters， thus affecting the accuracy of the estimation model[6]. In? response，? some? scientists? have? added texture? feature information to the? spectral features to improve? saturation when only? spectral informa‐tion is available， as well as to improve the spatialand? temporal? discrimination? of image? informationand the estimation potential of agronomic parame‐ters[7].

Some researchers have quantitatively analyzedthe application of texture features in agronomic pa‐rameters. Gu? et? al.[8]? studied? vegetation? coverageand found that the combination of vegetation indi‐ces and texture feature indices could improve the ac‐curacy? of? vegetation? coverage? estimation. Sarkerand Nichol[9]? assessed? the? biomass? of arbor? forestand found that the combination of vegetation indi‐ces and texture features was better at biomass esti‐mation. Cao et al.[10] extracted the texture featuresand? spectral? features? of thematic? mapper? imagesand? established? a? biomass? regression? estimationmodel，? which? could? effectively? estimate? the? bio ‐mass of mangrove wetlands. Mu et al.[11] constructeda multivariate regression model of vegetation indi‐ces， texture features， and vegetation biomass usingfour typical vegetation indices， and R2 of the modelwas 0.713， the minimum RMSE was 98.2543 g/m2.Based on the above researches， it was found that theaccuracy? of the? model? for? estimating? biomass? incombination with texture? features was higher thanthe? accuracy? of the model? for? estimating biomassusing? a? single? vegetation? index? with? color? fea‐tures[12].

In terms of monitoring crop growth based onunmanned aerial vehicle （UAV） images， Lee et al.[13]used RGB （Red Green Blue） cameras to obtain ricecanopy images and found that the absolute value ofgreen light depth （G） simulated by the exponentialequation was well correlated with the abovegroundbiomass and leaf area index （LAI）. Wang? et al.[14]demonstrated that the G-R value of rice RGB imag‐es had a good relationship with biomass and LAI，which could be used to construct an estimation mod‐el. Wang et al.[15] used random forest-support vector regression （RF-SVR） model? to? estimate? soil? and plant? analyzer? development （SPAD） values? of the wheat? canopy? and? achieved? high? accuracy （R2=0.754， RMSE=1.716）. Yue et al.[16] combined UAV remote? sensing? and AquaCrop? to? estimate? winter- wheat biomass， the predicted biomass agreed with the? measured? values （R2=0.61，? RMSE=2.10 t/ha）. Shan et al.[17] used explored the vertical distribution of wheat biomass and found that a linear regression relationship could be better obtained with a single row of wheat images， and the estimation accuracy was also higher than multi-row wheat image.

In this study， UAVs were used to obtain RGB images of wheat in different growth periods， and si‐multaneous sampling was conducted in the field to determine wheat biomass. The best color and tex‐ture feature indices were determined through corre‐lation analysis， and these indices were used to con ‐ struct? the? estimation? models? of wheat? biomass? in different growth periods based on UAV platform in order to provide a new method for biomass estima‐tion? and? real-time? monitoring? of wheat? growth? in the field.

2? Materials and methods

2.1 Field experiment design

Two experiments were conducted in this study， of which the data from Experiment 1 were used to construct a wheat biomass estimation model and the data from Experiment 2 were used for model valida‐tion. Study? sites were located in Yizheng city andZhangjiagang city， Jiangsu province.

2.1.1? Experiment 1 for estimation model???? Experiment 1 was conducted in Yizheng from

2018 to 2019. Two? wheat varieties? of Yangmai23 and Yangfumai4 were? selected as the research ob‐jects. Three planting densities were respectively setas：1 million plants/ha， 1.5 million plants/ha， and 2million plants/ha. Four nitrogen fertilizer levels wererespectively set as：0 kg/ha， 120 kg/ha， 160 kg/ha，and 200 kg/ha. Nitrogen fertilizers were applied ac‐cording? to? the? ratio? of base? fertilizer： new? shootsboosting fertilizer： jointing fertilizer： booting fertil‐izer =5：1：2：2， and the phosphorus and potassiumfertilizers? were? applied? according? to? the? ratio? ofbase fertilizer： jointing fertilizer =5：5， and the appli‐cation? amounts? all? were 120 kg/ha. Wheat? wasplanted on November 2， 2016， with a plot area of16.65 m2. Each treatment was repeated twice for atotal of 48 experimental plots.

2.1.2? Experiment 2 for model validation

Experiment 2 was conducted in Zhangjiagangfrom 2017 to 2018. The wheat variety， density， andfertilizer? were? the? same? as? Experiment 1. Wheatwas planted on November 10， 2017， with a plot areaof 30 m2. Each treatment was repeated twice for atotal of 48 experimental plots.

2.2 Data acquisition method

2.2.1? Image acquisition device

The Inspire 1 RAW UAV （DJI， Shenzhen， Chi‐na） was used for image data acquisition. This UAVis small， powerful， convenient and easy to operate，equipped with a 16-megapixel camera and is able tofly for 15-20 min depending on the load. The re‐mote control was connected to the wireless followerto extend the control distance to 5 km.

2.2.2? Image acquisition

UAV image? acquisition runs through the pre-wintering? period， jointing? period，? booting? period，and flowering period， and sampling time was about8：00-10：00 AM or 3：00-5：00 PM. In this study， DJIGS? Pro? was? used? to? automatically? generate? flightroutes? within? the? designated? area，? and? automaticflight，? automatic? shooting，? and? complete? relevantdata receiving， processing? and? sending. The? flight route of UAV was s-type， and the flight altitude was set to 15 m. In order to achieve accurate image reg ‐istration， the image repetition rate was set to 60% on the main route and 70% between the main route during route and point planning. The standard cali‐bration panels were used to perform radiation cali‐bration on RGB band sensors during flight to mini‐mize the influence of constantly changing lighting conditions on RGB images. After the aerial images were collected， the orthophoto image was generated by using the software Pix4DMapper， and the imag‐ es were stitched together seamlessly through feature matching of adjacent images[18].

2.2.3? Determination of aboveground biomass

In? the? pre-wintering? period，? jointing? period， booting? period，? and? flowering? period? of? wheat growth， 15 wheat? plants? were? selected? from? each plot， the aboveground parts of plants were collected and? transported? back? to? the? laboratory，? and? were dried in? an oven? for 1.5 h? at 105°C? and then? at 80°C until constant weight， and then were weighed and converted to biomass per unit area.

2.3 Data analysis and utilization

2.3.1? UAV image preprocessing method?? MATLAB2014a? software? was? used? for? UAVimage preprocessing， including image cropping， de‐noising，? smoothing，? and? sharpening. Image? crop ‐ping? stitches? the? images? into? uniform? images? ac‐cording to different cells. Denoising eliminates thenoise in digital images. Smoothing and sharpeningreduce the? slope of the image， improve the imagequality， and reduce the loss of pixel extraction fromthe target.

2.3.2 Color indices

Eight? commonly? used? color? indices? were? se‐lected for UAV image data analysis， including visualatmospheric resistance vegetation index （VARI）[19] ，excess red vegetation index （ExR）[20] ， excess greenvegetation index （ExG）[21] ， green leaf vegetation in ‐dex （GLI）[22] ，? excess? green-red? difference? index（ExGR）[20] ，? normalized? difference? index （NDI）[23] ，modified? green? red? vegetation? index （MGRVI）[24]and red green blue vegetation index （RGBVI）[24] ， asshown in Table 1.

2.3.3 Texture feature indices

MATLAB software was used to extract texture features? based? on? a? gray? level? co-occurrence? ma‐trix[25]. Four common texture features were extractedfrom? the? UAV? image： angular? second? moment（ASM）， contrast （CON）， correlation （COR） and en‐tropy （ENT）[25] ， as shown in Table 2.

2.4 Model construction and evaluation

Based on the correlation between image color and texture feature indices and wheat biomass， the color? and? texture? feature? indices? with? the? largest correlation coefficient were selected to construct the regression? model? for? biomass? estimation （Experi‐ment 1 data）. Then the independently measured bio ‐ mass? data? were? used? to? validate? and? evaluate? the model? based? on? the? coefficient? of? determination（R2）， root mean square error （RMSE）， and 1：1 map（Experiment 2 data）.

3? Results and analysis

3.1 Correlation? between? wheat? biomass and? image color/texture feature? indi ? ces in different growth periods

Wheat? biomass? experiences? various? changesthroughout the growth period and undergoes a pro ‐cess of continuous increase. In this study， the datain Experiment 1 were used to quantitatively analyzethe correlation between eight color indices as wellas four texture feature indices in the main growthperiods of wheat and biomass to determine the opti‐mal color index and texture feature index for esti‐mating biomass. The correlation between the differ‐ent color indices as well as the texture feature indi‐ces and wheat biomass based on the UAV image areshown in Table 3 and Table 4.

3.2 Biomass?? estimation?? models?? for wheat growth in different growth peri ? ods based on color indices

3.2.1? Model construction

（1） Estimation model of wheat biomass in thepre-wintering period

It can be seen from Table 1 that the correlationbetween? the? color? indices? of the? UAV? image? andbiomass was good during this period. Besides NDI，the correlation between the other seven indices andbiomass was extremely significant （P<0.01）， among which the correlation of VARI and biomass was the highest， with the correlation coefficient r reaching 0.743. Therefore， the color index VARI was chosen as the independent variable. The wheat biomass esti‐mation model was constructed by using regression analysis：

B1=535.9×VARI+87.9?? ????????????（13）

where B1 represents the biomass of wheat in the pre- winter period， kg/ha. The coefficient of determina‐tion R2 was 0.553.

（2） Estimation model of wheat biomass in the jointing period

The correlation between the color indices andbiomass? of the UAV? image? in? the jointing period was the highest， and eight color indices reached a highly? significant? correlation? level. Among? them， ExGR had the highest correlation with biomass， and the? correlation? coefficient? r reached 0.911. There‐ fore， the color index ExGR was selected as the inde‐ pendent variable， and the wheat biomass estimation model is：

B2=9054.6×ExGR+1915.5??????????? （14）

where? B2? represents? the? biomass? of wheat? in? the jointing period， kg/ha. It was constructed using re‐gression? analysis， with? a? coefficient? of determina‐tion R2 of 0.804.

（3） Estimation model of wheat biomass in the booting period

With? the? gradual? advancement? of? wheat growth and the increasing biomass， the color of the UAV image will become saturated， and its correla‐tion? with? biomass? will? be? correspondingly? weak‐ened. The correlation between UAV image color in ‐ dices and biomass in the booting period was signifi‐cantly lower than that in the jointing period. Only six? indices? were? significantly? correlated. Among them， MGRVI had the highest correlation with bio ‐mass，? and the? correlation? coefficient? r was 0.817.Therefore， the color index MGRVI was chosen? asthe independent variable， and the wheat biomass es‐timation model is：

B3=20024.1×MGRVI?543.2????????? （ 15）

where? B3? represents? the? biomass? of wheat? in? thebooting period， kg/ha. It was constructed using re‐gression analysis， with an R2 of 0.670.

（4） Estimation model of wheat biomass in theflowering period

The canopy image in the flowering period con ‐tains different types of objects such as the ear andleaf， and the correlation between image color indi‐ces and biomass is further reduced. During this peri‐od， the correlation between the UAV image color in ‐dices and biomass was the lowest among the fourperiods， three? of which being non-significant，? onebeing? significantly? correlated，? and? the? other? fourreaching extremely significant levels， of which thecorrelation? of VARI? and? biomass? was? the? highestand the correlation coefficient r was 0.679. There‐fore， the color index VARI was selected as the inde‐pendent variable， and the wheat biomass estimationmodel is：

B4=42623.1×VARI+7115.3?????????? （16）

where? B4? represents? the? biomass? of wheat? in? theflowering period， kg/ha. It was constructed using re‐gression analysis， and the R2 was 0.461.

3.2.2? Model validation

（1） Validation of the wheat biomass estimationmodel in the pre-wintering period

Independently measured data were used to vali‐date the wheat biomass estimation model in the pre-wintering period? and plot the 1：1 relationship be‐tween the measured values? and the model predic‐tions （Fig.1（a））. It is evident from the figure thatthere? was? high? agreement? between? the? predictedand measured values of wheat biomass in the pre-wintering period， and the predicted R2 of the modelwas 0.538. Correlation analysis showed that the cor‐ relation between the predicted value and the mea‐sured value reached a highly significant level， indi‐cating that the estimation model is feasible. In addi‐tion， the simulated RMSE was 27.88 kg/ha， which is relatively small， indicating that the simulation re‐sults of the model are relatively reliable.

（2） Validation of the wheat biomass estimation model in the jointing period

The validation results of the estimation model in the jointing period are shown in Figure 1（b）. The predicted value of wheat biomass in the jointing pe‐riod was close to the measured value， and there was high consistency between the two. The predicted R2 of the model was 0.631， and the effect was better than the pre-wintering period. Correlation? analysis indicated that the correlation between the predicted value and the measured value reached a highly sig ‐nificant level， indicating that the estimation model is? feasible. In? addition，? the? simulated? RMSE? was 516.99 kg/ha， which is relatively? small， indicating that the simulation results of the model are relative‐ly reliable.

（3） Validation of the wheat biomass estimation model in the booting period

The results of the estimation model in the boot‐ing period are shown in Figure 1（c）. It can be seen from? the? figure? that? the? predicted? value? of wheat biomass in the booting period was close to the mea‐sured? value，? and? there? was? high? consistency? be‐ tween the two. The predicted R2 of the model was 0.708， and the effect was better than the jointing pe‐riod. Correlation analysis indicated that the correla‐tion between the predicted value and the measured value was significant， indicating that the estimation model is feasible. In addition， the simulated RMSE was 868.26 kg/ha， which is relatively small， indicat‐ing that the simulation results of the model are rela‐tively reliable.

（4） Validation of the wheat biomass estimationmodel in the flowering period

The validation results of the estimation modelin the? flowering period? are? shown? in Figure 1（d）.The predicted value of wheat biomass in the flower‐ing? period? was? close? to? the? measured? value，? andthere was a good agreement between the two. Themodel predicted an R2 of 0.464， and the effect wasthe poorest? among the? four periods. However， thecorrelation analysis demonstrated a correlation be‐tween the predicted value and the measured value，indicating that the estimation model is feasible. Inaddition， the simulated RMSE was 1539.81 kg/ha，which is relatively small， indicating that the simula‐tion results of the model are relatively reliable.

In? summary，? the? UAV? image? color? indicescould be used to estimate wheat biomass in differ‐ent growth periods， and the effects in the differentperiods also differed. However， wheat biomass esti‐mation using? a? single? color index? did not achievethe best results.

3.3 Wheat biomass estimation models in different? growth? periods? based? on color and texture feature indices

3.3.1? Model construction

（1） Estimation model of wheat biomass in thepre-wintering period

It can be seen from Table 4 that the correlationbetween UAV image texture indices and biomass inthe pre-wintering period was high， however， it waspoorer than the color indices. Among the four tex‐ture feature indices， two were not significantly cor‐related，? whereas? the? other? two? were? significantlycorrelated， of which ASM had the highest correla‐tion? with? biomass，? with? a? correlation? coefficientof 0.573. Therefore，? the? texture? feature? index

ASM was combined with the color index VARI， andthe? wheat? biomass? estimation? multiple? regressionmodel is：

B5=547.9×VARI?35.9×ASM+97.2????? （17）

where B5 represents the biomass of wheat in the pre-winter period? combining VARI? and ASM. The R2was 0.565， and the accuracy was slightly improvedcompared with the single color index model （2.17%increase）.

（2） Estimation model of wheat biomass in thejointing period

From the pre-wintering period to the jointingperiod， the UAV image texture indices did not im‐prove significantly. Among the four texture featureindices， only two had a significant correlation withbiomass， with COR exhibiting the highest correla‐tion with an r of 0.574（Table 4）. Therefore， the tex‐ture feature index COR was combined with the col‐or index ExGR. The wheat biomass estimation mul‐tiple regression model is：

B6=8762.5×ExGR?259.4×COR+2075.8? （18）

where? B6? represents? the? biomass? of wheat? in? thejointing period combining ExGR and COR. The R2was 0.833，? and? the? accuracy? was? improved? com ‐pared with the single color index model （3.61% in ‐crease）.

（3） Estimation model of wheat biomass in thebooting period

The? colors? of the? wheat? UAV? images? in? thebooting period showed a certain saturation phenom ‐enon， and the texture feature indices were also af‐fected. Among the four texture feature indices， onlyCON? was? significantly? correlated? with? biomass，with an r of 0.417（Table 4）. Therefore， the texturefeature index CON was combined with the color in ‐dex MGRVI， and the wheat biomass estimation mul‐tiple regression model is：

B7=24027.4×MGRVI?3098.6×CON+2252.3（19）

where? B7? represents? the? biomass? of wheat? in? the booting period? combining MGRVI? and ?CON. The R2 was 0.762， and the accuracy was significantly im‐ proved compared with the single color index model （13.73% increase）.

（4） Estimation model of wheat biomass in the flowering period

The? correlation? between? UAV? image? texture feature indices and wheat biomass in the flowering period? was? relatively? low，? and? the? correlation? be‐ tween the four feature indices and wheat biomass did not reach a significant level （Table 4）. Therefore， the index ASM with the highest correlation coefficient was? selected? and? combined? with? the? color? index VARI. The wheat biomass? estimation? multiple re‐gression model is：

B8=42654.9×VARI?5595.3×ASM+8507.9（20） where? B8? represents? the? biomass? of wheat? in? the flowering period? combining VARI? and ASM. The R2 was 0.485. The accuracy was much higher than the single color index model （5.21% increase）.

3.3.2 Model validation

（1） Validation of the wheat biomass estimation model in the pre-wintering period

The independently measured data were used to validate the wheat biomass estimation model in the pre-wintering period，? and the 1：1 relationship be‐ tween the measured values? and the model predic‐tions? was? plotted （Figure 2（a））. There? was? good agreement? between? the? predicted? value? of wheat biomass and the measured value in the pre-winter‐ing? period. The? predicted? R2? of? the? model? was 0.571， which was better than the single color index model? （6.13%? increase）.? Correlation? analysis showed? that the? correlation between? the predicted value and the measured value was significant， indi‐cating that the estimation model is feasible. In addi‐tion， the simulated RMSE was 25.49 kg/ha， whichwas smaller than the RMSE of the single color in ‐dex model （reduced by 8.57%）， indicating that thereliability of the model had been further improved.

（2） Validation of the wheat biomass estimationmodel in the jointing period

The? validation? results? of the jointing? estima‐tion model are? shown in Fig.2（b）. It can be? seenthat? the? predicted? value? of wheat? biomass? in? thejointing? period? was? close? to? the? measured? value，and there was a good agreement between the two.The predicted R2 of the model was 0.658， which isbetter than the pre-wintering period， and the predic‐tion accuracy was improved compared to the singlecolor? index? model （4.28% increase）. Correlationanalysis? indicated that there was? a? correlation be‐tween the predicted value and the measured value，indicating that the estimation model is feasible. Thesimulated RMSE was 443.20 kg/ha， which was sig ‐nificantly smaller than the RMSE of the single col‐or index model （14.27% decrease）， indicating thatthe? reliability? of the? model? had? been? greatly? im‐proved.

（3） Validation of the wheat biomass estimationmodel in the booting period

The validation results of the booting period es‐timation model are shown in Fig.2（c）. The predict‐ed? value? of wheat? biomass? in? the? booting? periodwas close to the measured value， and there was highconsistency between the two. The model predictedan R2 of 0.753， which was better than the jointingperiod， and the prediction accuracy was greatly im‐proved? compared? to? the? single? color? index? model（6.36% increase）. Correlation? analysis? indicatedthat the correlation between the predicted value andthe measured value was significant， indicating thatthe? estimation? model? is? feasible. In? addition，? thesimulated? RMSE? was 816.25 kg/ha，? which? wassmaller? than? the? RMSE? of the? single? color? indexmodel （5.99% decrease）， indicating that the reliabili‐ ty of the model had been improved.

（4） Validation of the wheat biomass estimation model in the flowering period

The validation results of the flowering period estimation model are shown in Fig.2（d）. The pre‐dicted value of wheat biomass in the booting period was close to the measured value， and there was a high agreement between the two. The predicted R2 of the model was 0.515， and the prediction accuracy was? improved? compared to the? single? color? index model （10.99% increase）. Correlation analysis indi‐cated that the correlation between the predicted val‐ue and the measured value was significant， indicat‐ing that the? estimation model? is? feasible. In? addi‐tion，? the? simulated? RMSE? was 1396.97 kg/ha， which was smaller than the RMSE of the single col‐ or index model （reduced by 9.28%）， indicating that the reliability of the model had been improved.

Based on the above results， the biomass estima‐tion? model? of wheat? in? different? growth? periods based on the combination of UAV image color and texture? feature? indices? was? significantly? improved over the single color index model and can be used to estimate the biomass of wheat in different growth periods.

4? Discussion

At present， few studies have monitored growth indices such as wheat biomass using the RGB im‐ age data of UAVs. Hunt et al.[26] obtained wheat can ‐opy images using UAVs and used the vegetation in ‐ dices NDVI and GNDVI to monitor wheat growth in order to validate the availability of UAV images in wheat growth monitoring. In this study， the RGB images collected in the four key growth periods of wheat were systematically analyzed. The image in ‐ formation was? extracted，? and? eight? common? colorindices and four texture feature indices were calcu‐lated. The best performing color index and texture feature index were selected using correlation analy‐ sis. The results showed that the biomass estimation model constructed using a single color index could reduce the estimation accuracy due to the saturation of the image in the late growth period of wheat. In order to solve the above problems， the UAV image color and texture feature indices were combined to establish a multivariate model of wheat biomass es‐timation? based? on? two? types? of? indices，? and? the model? was? tested? using? independently ?measured biomass? data，? achieving? an? overall? better? perfor‐mance than a single color index model.

Other studies have combined image color indi‐ces with other indices to estimate crop yield or bio ‐ mass. For example， Lu et al.[27] used UAV image da‐ ta and point cloud data to estimate the aboveground biomass? of wheat. Compared? to? the use? of single color? data，? the? model? with? combined? indices? im‐ proved the estimation accuracy， R2 was 0.78， RMSE was 1.34 t/ha and rRMSE was 28.98%. Duan et al.[28] established a new method combining the vegetation indices based on UAV images and the abundance in ‐ formation? obtained? from? spectral? mixture? analysis （SMA）. The results showed that the vegetation in ‐dex? with? abundance? information? exhibited ?better predictive ability for rice yield than the vegetation index? alone? with? the? coefficient? of determination reaching 0.6 and estimation error below 10%. Zhou et al.[29] established wheat yield prediction model by using color and texture feature indices of RGB im‐ ages at wheat booting and flowering stages， and the R2 of the model increased by 2.15% and 3.69% com ‐ pared with the single-color index model， respective‐ly. The? above? results? demonstrate? that? combining the color indices of UAV images with different indi‐ces，? such? as? shape， texture，? and? coverage? can? im‐prove the estimation effect of the model.

In addition， previous studies often only moni‐tored the biomass in a certain growth period of thecrop due to image availability or other constraints，and thus the results? are difficult to? extrapolate. Inthis study， UAV images of the four key growth peri‐ods of wheat were analyzed， and the dynamic moni‐toring model of wheat biomass based on the UAVplatform was established. The model was tested us‐ing? independently? measured? data，? and? the? resultswere all reliable， indicating that it is feasible to dy‐namically monitor wheat biomass using a UAV plat‐form equipped with a digital camera， which is suit‐able for small and medium area applications. How ‐ever，? in? the? future，? UAVs? will? be? equipped? withmore? types? of? cameras. Therefore，? in? future? re‐search， information such as the spectrum， color， andtexture of the image could be comprehensively usedto improve the monitoring accuracy and range.

5 Conclusions

In this study， estimation models of wheat bio ‐mass at main growth stages were constructed basedon the color and texture index of UAV images.

Firstly， the color index and texture feature in ‐dex optimally correlated with wheat biomass wereidentified. Among the eight color indices， most ofthe? correlations? with? wheat? biomass? were? signifi‐cant， and the correlation of VARI in the pre-winter‐ing period was the highest at r=0.743**. The corre‐lation of ExGR in the jointing period was the high ‐est at r=0.911**. The correlation of MGRVI in thebooting period was hight at r=0.817**， and the cor‐relation of VARI in the flowering period was hightat r=0.679**. The correlation between the four im‐age texture feature indices and wheat biomass waspoor， and only a few indices reached significant cor‐relation levels.

Then， using the color indices of VARI， ExGR， MGRVI and VARI， the biomass estimation models of wheat in the pre-wintering， jointing， booting， and flowering periods were constructed， and the wheat biomass? estimation multivariate models were? con ‐ structed? by? combining? the? texture? feature? indices that? with? the? highest? correlation? with? wheat? bio ‐ mass.

Finally， the models were validated using inde‐pendently? measured? biomass? data. The? results showed? that both? two? types? of estimation? models passed? the? significance? test，? with? the? correlation reaching? a? significant? level? and? the? RMSE? being small. The? wheat biomass? estimation? model? com ‐bined with the UAV image color and texture feature indices was better than the single color index model.

References：

[1] WANG? E. Study? on? remote? sensing? monitoring? ofmaize growth and biomass[D]. Hefei： Anhui Agricul‐tural University， 2018.

[2] FAN Y， GONG Z， ZHAO W， et al. Study on vegetationbiomass? inversion method based? on hyperspectral re‐ mote? sensing[J]. Journal? of Hebei Normal University （Natural Science Edition）， 2016， 3：267-271.

[3] JIMENEZ-SIERRA? D，? CORREA? E，? BEN?TEZ-RE ‐STREPO? H，? et? al. Novel? feature-extraction? methods for? the? estimation? of? above-ground? biomass? in? rice crops[J]. Sensors， 2021， 21（13）：1-14.

[4] HOU X， NIU Z， HUANG N， et al. The hyperspectralremote sensing estimation models of total biomass and true LAI of wheat[J]. Remote Sensing for Land & Re‐ sources， 2012， 24（4）：30-35.

[5] LIU Q， PENG D， TU Y， et al. Estimating forest bio‐mass by partial least squares regression[J]. Journal of Northeast Forestry University， 2014， 7：44-47.

[6] YUE J， ZHOU C， GUO W， et al. Estimation of winter-wheat above-ground biomass using the wavelet analy‐ sis? of unmanned? aerial? vehicle-based? digital? images and hyperspectral crop canopy images[J]. International Journal of Remote Sensing， 2021， 42（5）：1602-1622.

[7] LI? J，? SHI Y， VEERANAMPALAYAM-SIVAKUMARA N， et al. Elucidating sorghum biomass， nitrogen and chlorophyll? contents with? spectral? and? morphological traits derived from unmanned aircraft system[J]. Fron‐tiers in Plant Science， 2018， 9：1-12.

[8] GU Z， JU W， LI L， et al. Using vegetation indices andtexture measures to estimate vegetation fractional cov‐erage （VFC） of planted and natural forests in Nanjingcity， China[J]. Advances in Space Research， 2013， 51（7）：1186-1194.

[9] SARKER L R， NICHOL J E. Improved forest biomassestimates using ALOS AVNIR-2 texture indices[J]. Re‐mote Sensing of Environment， 2011， 115（4）：968-977.

[10] CAO Q， XU D， JU H. The biomass estimation of man‐grove? community? based? on? the? textural? features? andspectral information of TM images[J]. Forest Resourc‐es Management， 2010， 6：102-108.

[11] MU? Q，? GAO? Z， BAO Y，? et? al. Estimation? of sparsevegetation biomass based on grey-level co-occurrencematrix of vegetation indices[J]. Remote Sensing Infor‐mation， 2016， 1：58-63.

[12] LIU C， YANG G， LI Z， et al. Biomass estimation inwinter wheat by UAV spectral information and textureinformation? fusion[J]. Scientia? Agricultura? Sinica，2018， 51（16）：3060-3073.

[13] LEE K J， LEE B W. Estimation of rice growth and ni‐trogen nutrition status using color digital camera imageanalysis[J]. European Journal of Agronomy， 2013， 48（3）， 57-65.

[14] WANG Y， WANG D， ZHANG G， et al. Estimating ni‐trogen status of rice using the image segmentation ofG-R? thresholding? method[J]. Field? Crops? Research，2013， 149（149）：33-39.

[15] WANG J， ZHOU Q， SHANG J， et al. UAV- and ma‐chine learning-based retrieval of wheat SPAD values atthe? overwintering? stage? for? variety? screening[J]. Re‐mote sensing， 2021， 13：1-19.

[16] YUE J， FENG H， LI Z， et al. Mapping winter-wheatbiomass? and? grain? yield based? on? a? crop? model? andUAV? remote? sensing[J]. International? Journal? of Re‐mote Sensing， 2021， 42（5）：1577-1601.

[17] SHAN C， LIAO S， GONG Y， et al. Application of digi‐tal image processing for determination of vertical dis‐tribution of biomass in the canopy of winter wheat[J].Acta AgronomicaSinica， 2007， 33（3）：419-424.

[18] CHEN W. Evsluation of seed emergence unifermity ofwheat based on UAV image[D]. Yangzhou： YangzhouUnivefrsity， 2018.

[19] GITELSON A A， STARK R， GRITS U， et al. Vegeta‐tion and soil lines in visible spectral space： A conceptand technique for remote estimation of vegetation frac‐tion[J]. International Journal of Remote Sensing， 2002，23（13）：2537-2562.

[20] MAO D， WU X， DEPPONG C， et al. Negligible roleof antibodies and C5 in pregnancy loss associated ex‐clusively with C3-dependent mechanisms through com ‐plement? alternative? pathway[J]. Immunity， 2003， 19（6）：813-822.

[21] WOEBBECKE D M， MEYER G E， VON BARGENK， et al. Plant species identification， size， and enumera‐tion? using? machine? vision? techniques? on? near-binary images[J]. Proceedings of SPIE-The International Soci‐ety for Optical Engineering， 1993， 1836：1-12.

[22] HUNT JR E R， DAUGHTRY C S T， EITEL J U H， etal. Remote sensing leaf chlorophyll content using a vis‐ible? band? index[J]. Agronomy? Journal， 2011， 103（4）：1090-1099.

[23] TUCKER P W， HAZEN E E， COTTON F A. Staphylo‐coccal nuclease reviewed： A prototypic? study in? con‐ temporary? enzymology[J]. Molecular & Cellular Bio‐ chemistry， 1979， 23（2）：67-86.

[24] BENDIG J， YU K， AASEN H， et al. Combining UAV-based plant height from crop? surface models， visible， and near infrared vegetation indices for biomass moni‐toring? in? barley[J]. International? Journal? of Applied Earth Observation & Geoinformation， 2015， 39：79-87.

[25] HARALICK R M，? SHANMUGAM K， DINSTEIN I.Textural? features? for? image? classification[J]. IEEETransactions on Systems Man & Cybernetics， 1973， 3（6）：610-621.

[26] HUNT JR E R ， HIVELY W D， FUJIKAWA S， et al.Acquisition? of? NIR-Green-Blue? digital? photographsfrom? unmanned? aircraft? for? crop? monitoring[J]. Re‐mote Sensing， 2010， 2（1）：290-305.

[27] LU N， ZHOU J， HAN Z，? et? al. Improved? estimationofaboveground biomass? in wheat? from RGB? imageryand point cloud data acquired withalow?cost unmannedaerial vehicle system[J]. Plant Methods， 2019， 15：1-16.

[28] DUAN B， FANG S， ZHU R， et al. Remote estimationof rice yield with unmanned aerial vehicle （UAV） dataand spectral mixture analysis[J]. Frontiers in Plant Sci‐ence， 2019， 10：1-14.

[29] ZHOU? Y，? WANG? D，? CHEN? C，? et? al. Prediction? ofwheat yield based on color index and texture feature in‐dex of UAV RGB image[J]. Journal of Yangzhou Uni‐versity （Agricultural and Life? Science Edition）， 2021，42（3）：110-116.