基于垄间背景剔除优化小麦赤霉病遥感监测精度

为探究麦田垄间背景对无人机多光谱小麦赤霉病监测精度的影响，该研究以江苏省镇江市农科院灌浆期小麦为研究对象，利用大疆M600 Pro无人机搭载RedEdge-MX多光谱相机获取小麦冠层多光谱影像。通过筛选与小麦赤霉病相关性最高的植被指数（vegetation indexes，VIs）：MSR和CRI2植被指数，并采用大津法（Nobuyuki Otsu method，OTSU）、阈值分割法和支持向量机（support vector machine，SVM）等方法对小麦赤霉病遥感图像进行精细化语义分割，降低田块边缘阴影背景和染病麦穗之间的误判率。试验结果表明：目视解译阈值分割法剔除背景的效果最好（总体精度：92.06 %，Kappa系数：0.84），OTSU阈值分割法（总体精度：90.52%，Kappa系数：0.81）效果次之。采用偏最小二乘回归分别建立小麦病情指数（disease index，DI）与VIs、纹理特征（texture features，TFs）和VIs&TFs小麦赤霉病监测模型，其中VIs&TFs模型监测精度最高，剔除垄间背景前预测模型训练集的决定系数（coefficient of determination，R2）为0.73，均方根误差（root mean square error，RMSE）为5.52，相对分析误差（relative percent deviation，RPD）为2.01，验证集的R2为0.68，RMSE为6.21，RPD为1.96；剔除垄间背景后VIs&TFs模型监测精度依然最高，训练集的R2为0.75，RMSE为5.58，RPD为2.13，验证集的R2为0.77，RMSE为7.13，RPD为2.11。综上所述，基于垄间背景特征的精细化语义分割有效地提高了小麦赤霉病的监测精度，可以直观地了解小麦病情分布情况，可对后续变量施药提供参考依据。

Optimizing wheat scab in remote sensing monitoring accuracy using interridge background elimination

Wheat scab has been one of the most severe diseases for grains in recent years. Unmanned aerial vehicles (UAV) can be used to monitor the wheat scab using multispectral remote sensing. In this study, a series of operations were carried out to eliminate the inter-ridge background elements (such as soil and shadow intercage background elements) under natural conditions. The precision segmentation of inter-ridge background was then realized to improve the identification accuracy of wheat scab. A high-precision monitoring model was also constructed for the wheat scab. Specifically, the research object was selected as the wheat at the filling stage in the Zhenjiang Academy of Agricultural Sciences in Jiangsu Province, China. Rededge MX multispectral camera (five channels) was installed on a DJI M600 Pro six-rotor UAV to capture the multispectral images of the wheat canopy. Multiple vegetation indices were then calculated using the spectral features of the wheat canopy. A correlation analysis was implemented to determine the highest correlation vegetation indices (VIs) with the wheat scab, and the gray scale co-occurrence matrix (gray-level co-occurrence matrix, GLCM and texture feature (TFs). Otus, threshold segmentation and support vector machine (SVM) were used for the semantic segmentation of wheat scab multispectral images. There was a reduced influence of field edge shadow and soil background elements on the identification accuracy of wheat scab. Partial least squares regression (PLSR) was used after eliminating the ridge background. The regression model was established for the VIs, TFs, Vis & TFs and wheat scab disease index (DI). The regression equation of the monitoring model was then constructed to draw the change chart of disease severity. The experimental results show that the best performance was achieved in the visual interpretation threshold segmentation (overall accuracy: 92.06%, Kappa coefficient: 0.84), and the OTSU threshold segmentation (overall accuracy: 90.52%, Kappa coefficient: 0.81) in the inter-row background elimination model. In the wheat scab monitoring model, the accuracy of the Vis&TFs model was higher than that of the VIs and TFs model, and the accuracy of the refined semantic segmentation model was better than that of the unsegmentation model. Taking the model on May 8 as an example, the training set and validation set R2 of the VIs-PLSR undivided model were 0.71 and 0.73, RMSE were 5.61 and 6.72, and RPD were 1.83 and 1.89, respectively. The training set and validation set R2 of the TFs-PLSR undivided model were 0.64 and 0.62, RMSE were 6.03 and 6.92, and RPD were 1.67 and 1.69, respectively. Vis&TFs undivided model training set and validation set R2 were 0.73 and 0.61, RMSE were 6.31 and 6.68, RPD were 1.93 and 1.89, respectively. The training set and validation set R2 of VIs&TFs fine segmentation model were 0.78 and 0.81, RMSE were 4.73 and 4.31, and RPD were 2.26 and 2.05. In summary, the refined semantic segmentation with the inter-ridge background feature effectively improved the accuracy of the wheat scab monitoring model. The monitoring model was realized to classify the diseases, and then to generate the spatial distribution map of wheat scab grade. The disease distribution of wheat scab was obtained to predict the trend of wheat scab diseases. The finding can also provide effective guidance for the variable application in the late stage of wheat cultivation.