By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution were combined to construct the model, and its overall accuracy, sensitivity, and generalization ability were evaluated. First, the sensitive spectral features and bands of the UAV-derived hyperspectral images were obtained, and then texture features were selected. Subsequently, spectral features and texture features extracted from windows of different sizes were input to classify the area of severe FHB. According to the model comparison, the optimal window size was obtained. With the collinearity between features eliminated, the best performance of the logistic model reached, with an accuracy, F1 score, and area under the receiver operating characteristic curve of 0.90, 0.79, and 0.79, respectively, when the window size of the GLCM was 5×5 pixels on May 3, and of 0.90, 0.83, and 0.82, respectively, when the size was 17×17 pixels on May 8. The results showed that the selection of an appropriate GLCM window size for texture feature extraction enabled more accurate disease detection.

中文翻译：

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在最佳窗口尺寸下使用基于无人机的光谱和纹理特征检测小麦镰刀菌枯萎病

经过 结合无人机（UAV）拍摄图像的光谱和纹理特征，可以实现小麦枯萎病（FHB）的准确及时检测。本研究提出了一种选择灰度共生矩阵 (GLCM) 的最佳窗口大小的方法，以从无人机图像中提取纹理特征以进行 FHB 检测。结合宿主条件和疾病分布构建模型，并对其整体准确性、敏感性和泛化能力进行评估。首先获取无人机高光谱图像的敏感光谱特征和波段，然后选择纹理特征。随后，输入从不同大小的窗口中提取的光谱特征和纹理特征，对严重的 FHB 区域进行分类。根据模型对比，获得最佳窗口大小。消除特征之间的共线性，当GLCM的窗口大小为5时，逻辑模型的最佳性能达到了，准确率、F1分数和受试者工作特征曲线下面积分别为0.90、0.79和0.79 5 月 3 日×5 像素，5 月 8 日大小为 17×17 像素时分别为 0.90、0.83 和 0.82。结果表明，选择合适的 GLCM 窗口大小进行纹理特征提取可以实现更准确的疾病检测。