State Key Laboratory of Wheat and Maize Crop Science, Agronomy College, Henan Agriculture University, Zhengzhou 450046, China.
Information and Management Science College, Henan Agricultural University, Zhengzhou 450046, China.
Sensors (Basel). 2021 Dec 22;22(1):31. doi: 10.3390/s22010031.
Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.
白粉病严重影响小麦生长和产量；因此，有效监测白粉病对于疾病的预防和控制以及全球粮食安全至关重要。在本研究中，使用光谱辐射计和热红外摄像机获取高光谱特征和热红外图像数据，并在小麦开花和灌浆期，从感病小麦的热红外图像和 RGB 图像中提取热红外温度参数 (TP) 和纹理特征 (TF)。基于高光谱数据的十个植被指数 (VI)，整合 TF 和 TP，并采用偏最小二乘回归、随机森林回归 (RFR) 和支持向量机回归 (SVR) 算法构建小麦白粉病病情指数预测模型。结果表明，在单数据源建模和多数据源建模下，RFR 的预测精度均高于其他模型；在三种数据源中，VI 最适合白粉病监测，其次是 TP，最后是 TF。RFR 模型在多源数据融合建模 (VI&TP&TF) 中具有稳定的性能，其校准和验证的 R 分别为 0.872 和 0.862，具有最佳的估计性能。多源数据协同建模的应用可以提高小麦白粉病遥感监测的准确性，有助于实现作物病虫害状况的高精度遥感监测。
