UAV Digital Image-assisted Monitoring on Nitrogen Nutrition of Winter Wheat in the Field
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摘要:
目的 准确、快速地获取作物的氮素信息，对监测作物氮素营养状况、指导变量施肥具有重要意义。 方法 获取了冬小麦挑旗期及开花期数码影像和相应的冬小麦地面农学参数，首先分析了数码图像指数与氮营养指数的相关性，然后结合相关系数和方差膨胀因子，筛选出对氮营养指数敏感且图像指数间不存在共线性的图像指数，通过偏最小二乘法建立各生育期氮营养诊断模型，并利用挑旗期和开花期的诊断模型对无人机影像进行填图和可视化。 结果 结合相关系数和方差膨胀因子筛选出挑旗期的图像指数分别是b、g/b、（r−g−b）/（r+g）、NDI、WI，筛选出开花期的图像指数分别是b、r/b、（r−g−b）/（r+g）、VARI。就生育期而言，开花期建模的决定系数比挑旗期的决定系数高0.008 8，均方根误差低0.021 7，开花期可以较好地反应冬小麦氮素营养状况。 结论 挑旗期和开花期的数码影像，经填图和可视化处理后得到的氮营养指数分布图能较好地监测不同生育期氮素营养状况，为田间小麦氮素营养状况监测提供高效的技术手段。 Abstract:Objective Utilization of digital images taken by unmanned aerial vehicle (UAV) to quickly and accurately monitor the nitrogen nutrition of winter wheat crops in the field for fertilization management was explored. Method The digital images of field winter wheat at flagging and flowering stages obtained by camera on a UAV along with the agronomic information of the crop on the ground were collected. A correlation analysis on the data was conducted, and the variance inflation factors integrated for index selection. The selected indices sensitive to the variations of nitrogen nutrition indicators (NNIs) and free of co-linearity among themselves were employed to develop a mathematic model on nitrogen nutrition using the partial least square regression method and verified with the collected data for prediction accuracy and applicability. Result The correlation coefficient and variance inflation factor allowed a precise index selection from the digital images. The indices for the winter wheat at flagging stage were thus determined to include b, g/b, (r-g-b)/(r+g), NDI, and WI, while those at flowering stage b, r/b, (r-g-b)/(r+g), and VARI. The coefficient on the model at the flowering stage was 0.008 8 higher, and the root mean square error 0.021 7 lower, than those at the flagging stage. Conclusion Using the UAV digital images of the winter wheat at flagging and flowering stages, a visualized distribution map of the indices was constructed to enable a clear and accurate display of the nitrogen nutrition status of the crop in the field. The results at the flowering stage were considered slightly more sensitive than those obtained at the flagging stage in providing information on fertilization management. -
图 2 各生育期图像指数之间的方差膨胀因子
注：A：挑旗期；B：开花期。
Figure 2. Variance inflation factor of image indices at flagging and flowering stages
Note: A: Flagging; B: Flowering stage. The same as below.
图 3 冬小麦各生育期NNI实测值与偏最小二乘模型估测值关系
Figure 3. Correlation between measured and estimated values of NNIs of winter wheat at each growth stage by partial least squares model
图 4 冬小麦各生育期NNI预测值
Figure 4. Estimated values of NNIs of winter wheat at each growth stage
表 1 研究用到的图像指数
Table 1. Image indices applied
图像指数
Image index公式
Formula参考文献
Referencer r=R/（R+G+B） / g g=G/（R+G+B） / b b=B/（R+G+B） / r/b r/b * g/b g/b * r−b r−b * g+b g+b * g−b g−b * IKAW IKAW= （r−b）/（r+b） 文献[29] 三波段植被指数（r−g−b）/（r+g） （r−g−b）/（r+g） * 超绿指数 ExG ExG=2×g−b−r 文献[30] 红绿植被指数 GRVI GRVI=（g−r）/（g+r） 文献[31] 修正红绿植被指数 MGRVI MGRVI=（g2−r2）/（g2+r2） 文献[32] 红绿蓝植被指数 RGBVI RGBVI=（g2−b×r）/（g2+ b×r） 文献[32] 超红指数ExR ExR=1.4×r−g 文献[30] 归一化差异植被指数 NDI NDI=（r−g）/（r+g+0.01） 文献[33] 大气阻抗植被指数 VARI VARI=（g−r）/（g+r−b） 文献[34] 超绿超红差分指数 EXGR EXGR=3×g−2.4×r−b 文献[30] 沃贝克指数 WI WI=（g−b）/（r−g） 文献[32] r/g r/g * r+b r+b * 地平面影像指数 GLA GLA=（2×g−r+b）/（2×g+r+b） 文献[35] 地平面影像指数 GLI GLI=（2×g−r−b）/（2×g+r+b） 文献[35] 超红绿指数 ExGR ExGR=ExG−1.4×r−g 文献[33] 彩色植被指数 CIVE CIVE=0.441×r−0.881×g+0.3856×b+18.78745 文献[36] 注：“ / ” 表示数码影像三个波段R、G、B分别归一化后的值，“ * ” 表示经验的图像指数。
Note: " / " divides normalized R, G and B band values of digital image; " * " represents measured indices.
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表 2 不同生育期图像指数与氮营养指数的相关性
Table 2. Correlation coefficients between image indices and NNIs at two growth stages of winter wheat crop
图像指数
Image index生育期
Growth stage挑旗期
Flagging stage开花期
Flowering stager −0.804 0** −0.710 3** g 0.313 4 0.370 1** b 0.855 7** 0.783 4** r/b −0.855 2** −0.813 7** g/b −0.738 0** −0.289 6 r−b −0.848 2** −0.806 5** g+b 0.804 0** 0.710 3** g−b −0.660 1** −0.148 6 IKAW −0.851 3** −0.818 4** （r−g−b）/（r+g） −0.820 0** −0.746 0** ExG 0.313 4 0.370 0** GRVI 0.711 5** 0.595 9** MGRVI 0.711 4** 0.594 7** RGBVI 0.190 0 0.335 2 ExR −0.729 8** −0.610 6** NDI −0.711 4** −0.595 7** VARI 0.729 8** 0.621 5** EXGR −0.574 1** 0.491 9** WI 0.644 6** −0.058 5 r/g −0.711 0** −0.588 5** r+b −0.313 4 −0.370 1** GLA 0.777 6** 0.668 0** GLI 0.314 4 0.370 3** ExGR 0.664 3** 0.552 7** CIVE −0.365 8** −0.391 2** 注：**表示相关性在0.01水平下达到显著。
Note: ** represents correlation reached a significant level at 0.01.
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表 3 建模与验证
Table 3. Modeling and validation
生育期
Growth period建模
Modeling验证
ValidationR2 RMSE MAE R2 RMSE MAE 挑旗期 Flagging stage 0.711 6 0.107 5 0.086 0 0.764 1 0.135 8 0.105 2 开花期 Flowering stage 0.720 4 0.085 8 0.071 7 0.798 9 0.187 1 0.131 0
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