基于不同地面分辨率的无人机图像监测水稻白叶枯

    Monitoring rice bacterial blight based on UAV images of different ground sampling distances (GSD)

    • 摘要:
      目的 快速无损地监测水稻白叶枯并量化感染程度,从而指导田间作业。
      方法 利用无人机获取受白叶枯病菌Xanthomonas oryzae pv. oryzae感染的水稻冠层高分辨率图像,提取颜色和纹理特征,分别构建基于颜色、纹理特征以及两者融合的多元回归模型,对白叶枯感染等级进行预测。探究不同地面分辨率(Ground sampling distances,GSD)对不同模型精度的影响。
      结果 基于颜色特征的监测模型的决定系数(Coefficient of determination,R2)为85.9%,均方根误差(Root mean square error,RMSE)为1.43,相对均方根误差(Relative RMSE,RRMSE)为19.1%,相比基于纹理特征的模型,R2上升了2.4个百分点,RRMSE增加了4.6个百分点;与单一种类特征相比,基于颜色和纹理特征融合的预测模型(R2=89.6%,RMSE=1.06,RRMSE=15.1%)精度有较大的提升;通过构建不同GSD模型发现,当GSD为0.2 、0.5或0.8 cm时,模型精度较高,R2 均在80%以上。
      结论 从无人机捕获的低空遥感图像中提取的颜色和纹理特征可用于监测水稻白叶枯病,结果可为无人机遥感监测水稻白叶枯提供有效的技术支持。

       

      Abstract:
      Objective In order to monitor rice bacterial blight quickly and non-destructively, and guide field operations.
      Method High-resolution images of rice canopy under bacterial blight stress were acquired using utilized unmanned aerial vehicles (UAVs). Color features and texture features were extracted from the images, and multiple regression models based on color features, texture features, and the fusion of color and texture features were constructed to predict the infection level of rice bacterial blight. The influence of different ground sampling distances (GSD) on the accuracy of the models was also explored.
      Result The determination coefficient (R2) of the monitoring model based on color features was 85.9%, root mean square error (RMSE) was 1.43 and relative RMSE (RRMSE) was 19.1%. The R2 had increased by 2.4 percentage points and RRMSE had increased by 4.6 percentage points compared with the model based on texture features. Compared with single-feature models, the prediction model based on the fusion of color and texture features (R2=89.6%, RMSE=1.06, RRMSE=15.1%) exhibited significant improvement in accuracy. By constructing models with different GSDs, it was found that when the GSD was 0.2, 0.5 or 0.8 cm, the models achieved higher accuracy with R2 all above 80%.
      Conclusion The color and texture features extracted from low-altitude remote sensing images captured by UAVs can be used for monitoring rice bacterial blight. The results can provide effective technical support for UAV remote sensing monitoring of rice bacterial blight.

       

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