Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (16): 3417-3427.doi: 10.3864/j.issn.0578-1752.2021.16.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Research on Winter Wheat Yield Estimation Based on Hyperspectral Remote Sensing and Ensemble Learning Method

FEI ShuaiPeng1,2(),YU XiaoLong2,LAN Ming2,LI Lei2,XIA XianChun2,HE ZhongHu2,3,XIAO YongGui2()   

  1. 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, Henan
    2Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081
    3CIMMYT-China Office, c/o CAAS, Beijing 100081
  • Received:2020-11-18 Accepted:2021-04-08 Online:2021-08-16 Published:2021-08-24
  • Contact: YongGui XIAO E-mail:feishuaipeng@163.com;xiaoyonggui@caas.cn

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Abstract:

【Objective】Using the hyperspectral data of winter wheat canopy at different development stages under two irrigation treatments, the estimation accuracy of wheat grain yield was studied by machine learning method, and the best yield estimation model was defined, which had the important application value for crop breeding. 【Method】 A total of 207 widely-grown wheat varieties in the Yellow and Huai Valleys Winter Wheat Zone (YHVWWZ) of China were planted under full irrigation and limited irrigation treatments in Xinxiang, Henan province during two consecutive growing seasons of 2018-2019 and 2019-2020, the canopy hyperspectral was investigated at three growth stages after flowering, and six machine learning methods and ensemble methods were adopted to establish yield prediction model by using spectral index as input features.【Result】 The spectral indices at each growth stage were significantly correlated with yield (P<0.0001) under both the two irrigation treatments, and also showed high heritability (0.61-0.85) across all the three growth stages under both the irrigation treatment, which were mainly controlled by genetic factors. Under the full irrigation treatment, compared with the model with the best performance of traditional machine learning methods, the average coefficient of determination (R2) of ensemble learning method in the three growth stages increased from 0.610, 0.611 and 0.640 to 0.649, 0.612 and 0.675, respectively, and the average root mean square error (RMSE) decreased to 0.607, 0.612 and 0.593 t·hm-2, respectively; Under the limited irrigation treatment, the average R 2 increased from 0.461, 0.408 and 0.452 to 0.467, 0.433 and 0.498, respectively, and the average RMSE decreased to 0.519, 0.559 and 0.504 t·hm -2, respectively.【Conclusion】Combining the prediction results of different models with the ensemble learning method could effectively improve the yield estimation accuracy, and the mid grain filling achieved the best prediction accuracy under both the two irrigation treatments. Overall, this study could provide the reference for yield estimation in winter wheat breeding.

Key words: winter wheat, grain yield, hyperspectral, ensemble method, machine learning

Table 1

Spectral indices used in this study"

光谱指数 Spectral index 名称 Name 公式 Formula
NDVI[23] 归一化光谱指数
Normalized difference vegetation index		 $\frac{R_{800}-R_{670}}{R_{800}+R_{670}}$
MCARI[24] 修正叶绿素吸收比指数
Modified chlorophyll absorption ratio index		 $\left[\left(R_{702}-R_{671}\right)-0.2\left(R_{702}-R_{549}\right)\right] \times \frac{R_{702}}{R_{671}}$
NDRE[25] 归一化红边光谱指数
Normalized difference red edge		 $\frac{R_{790}-R_{720}}{R_{790}+R_{720}}$
GNDVI[26] 绿色归一化光谱指数
Green normalized difference vegetation index		 $\frac{R_{750}-R_{550}}{R_{750}+R_{550}}$
MSR[23] 修正红边比值指数
Modified simple ratio index		 $\frac{R_{750} / R_{705}-1}{\sqrt{R_{750} / R_{705}+1}}$
NDRSR[27] 归一化红边简单比值指数
Normalized difference red-edge simple ratio		 $\frac{R_{872}-R_{712}}{R_{872}+R_{712}}$
MTVI[28] 修正三角光谱指数
Modified triangular vegetation index		 1.2[1.2(R800-R500)-2.6(R670-R550)]
MTCI2[29] MERIS陆地叶绿素指数2
MERIS terrestrial chlorophyll index 2		 $\frac{R_{754}-R_{709}}{R_{709}+R_{681}}$
MNDVI[30] 修正归一化光谱指数
Modified normalized difference vegetation index		 $\frac{R_{750}-R_{705}}{R_{750}+R_{705}-2 R_{445}}$
RDVI[31] 重归一化光谱指数
Renormalized difference vegetation index		 $\frac{R_{800}-R_{670}}{\sqrt{R_{800}+R_{670}}}$
VDI[32] 植被干指数
Vegetation dry index		 $\frac{R_{970}-R_{900}}{R_{970}+R_{900}}$
CI[33] 叶绿素指数
Chlorophyll index		 (R749-R720)-(R701-R672)
VREI[34] 沃格尔曼红边指数
Vogelmann red edge index		 $\frac{R_{742}}{R_{722}}$
ARVI[35] 大气抗性光谱指数
Atmospherically resistant vegetation index		 $\frac{R_{872}-\left[R_{661}-\left(R_{488}-R_{661}\right)\right]}{R_{872}+\left[R_{661}-\left(R_{488}-R_{661}\right)\right]}$
NDMI[36] 归一化物质指数
Normalized difference matter index		 $\frac{R_{1649}-R_{1792}}{R_{1649}+R_{1792}}$

Fig. 1

Flow chart for establishing grain yield estimation model based on Stacking method"

Table 2

Correlation analysis of spectral index and grain yield under full irrigation treatment, spectral index heritability"

光谱指数
Spectral index		 开花期Flowering 灌浆前期Early grain filling 灌浆中期Mid grain filling
|r| H2 |r| H2 |r| H2
NDVI 0.50*** 0.83 0.53*** 0.75 0.66*** 0.74
MCARI 0.61*** 0.85 0.65*** 0.85 0.69*** 0.82
NDRE 0.71*** 0.81 0.65*** 0.79 0.72*** 0.78
GNDVI 0.68*** 0.82 0.63*** 0.78 0.71*** 0.77
MSR 0.65*** 0.80 0.62*** 0.76 0.70*** 0.76
NDRSR 0.72*** 0.82 0.67*** 0.79 0.73*** 0.79
MTVI 0.59*** 0.77 0.60*** 0.73 0.63*** 0.75
MTCI2 0.63*** 0.83 0.59*** 0.80 0.68*** 0.80
MNDVI 0.62*** 0.83 0.63*** 0.76 0.69*** 0.83
RDVI 0.60*** 0.77 0.62*** 0.77 0.66*** 0.78
VDI 0.45*** 0.84 0.43*** 0.80 0.62*** 0.83
CI 0.61*** 0.82 0.59*** 0.79 0.64*** 0.81
VREI 0.69*** 0.81 0.65*** 0.75 0.72*** 0.76
ARVI 0.52*** 0.79 0.54*** 0.73 0.65*** 0.73
NDMI 0.53*** 0.80 0.56*** 0.63 0.61*** 0.77

Table 3

Correlation analysis of spectral index and grain yield under limited irrigation treatment, spectral index heritability"

光谱指数
Spectral index		 开花期 Flowering 灌浆前期 Early grain filling 灌浆中期 Mid grain filling
|r| H2 |r| H2 |r| H2
NDVI 0.48*** 0.63 0.42*** 0.69 0.53*** 0.65
MCARI 0.57*** 0.64 0.49*** 0.70 0.56*** 0.68
NDRE 0.56*** 0.67 0.48*** 0.78 0.55*** 0.73
GNDVI 0.54*** 0.66 0.41*** 0.73 0.54*** 0.71
MSR 0.55*** 0.65 0.43*** 0.74 0.52*** 0.69
NDRSR 0.57*** 0.68 0.49*** 0.79 0.55*** 0.73
MTVI 0.45*** 0.64 0.46*** 0.68 0.49*** 0.68
MTCI2 0.50*** 0.62 0.45*** 0.71 0.50*** 0.66
MNDVI 0.52*** 0.64 0.49*** 0.67 0.51*** 0.71
RDVI 0.49*** 0.66 0.47*** 0.65 0.50*** 0.67
VDI 0.59*** 0.69 0.61*** 0.68 0.58*** 0.73
CI 0.48*** 0.64 0.49*** 0.74 0.52*** 0.74
VREI 0.55*** 0.68 0.48*** 0.68 0.52*** 0.69
ARVI 0.49*** 0.61 0.42*** 0.71 0.51*** 0.68
NDMI 0.44*** 0.73 0.49*** 0.66 0.50*** 0.72

Fig. 2

The R2 (a) and RMSE (b) distribution on the test set during cross-validation of six primary models at flowering"

Fig. 3

The R2 (a) and RMSE (b) distribution on the test set during cross-validation of six primary models at early grain filling"

Fig. 4

The R2 (a) and RMSE (b) distribution on the test set during cross-validation of six primary models at mid grain filling"

Fig. 5

R2 (a) and RMSE (b) distribution on the test set during cross-validation of secondary model"

Table 4

The average coefficients of each primary learner in the modeling of the secondary learner"

模型
Model		 正常灌溉处理 Full irrigation treatment 节水处理 Limited irrigation treatment
开花期
Flowering		 灌浆前期
Early grain filling		 灌浆中期
Mid grain filling		 开花期
Flowering		 灌浆前期
Early grain filling		 灌浆中期
Mid grain filling
ANN -4.53 0.43 -0.59 -4.10 -2.81 0.93
GP 0.24 0.26 -1.85 2.10 0.77 0.86
MLR 1.91 -0.19 4.51 -2.14 -0.14 -2.02
RF 0.84 -0.30 -7.30 -0.47 0.02 -0.37
RR 6.13 0.61 4.32 3.20 4.06 3.28
SVM -3.41 0.42 2.34 2.43 -0.56 -1.59
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