Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion.
Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion.
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DOI：
10.3390/s22010031
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发表时间：
2021-12-22
期刊：
影响因子：
--
通讯作者：
Feng W
中科院分区：
文献类型：
--
作者：
Feng Z;Song L;Duan J;He L;Zhang Y;Wei Y;Feng W
白粉病严重影响小麦生长和产量，因此，对白粉病的有效监测对防治和保障全球粮食安全至关重要。本研究利用光谱仪和热红外相机获取小麦开花期和灌浆期的高光谱特征和热红外图像数据，并从小麦白粉病的热红外图像和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10个植被指数(VI)，综合因子和TP，利用偏最小二乘回归、随机森林回归(RFR)和支持向量机回归(SVR)算法构建了小麦白粉病指数的预测模型。结果表明，无论是在单一数据源建模还是在多源数据建模下，RFR的预测精度都高于其他模型；在三个数据源中，VI最适合白粉病监测，其次是TP，最后是TF。该模型在多源数据融合建模(VI&TP&TF)中性能稳定，具有最优的估计性能，校正和验证的R2分别为0.872和0.862。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精度，有利于实现作物病害状况的高精度遥感监测。
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 R2 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.
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影响因子：
13.5
作者：
Calderon, R.;Navas-Cortes, J. A.;Zarco-Tejada, P. J.
通讯作者：
Zarco-Tejada, P. J.
影响因子：
8.3
作者：
Chandel, Abhilash K.;Khot, Lav R.;Yu, Long-Xi
通讯作者：
Yu, Long-Xi
DOI：
10.3964/j.issn.1000-0593(2010)02-0512-06
发表时间：
2010-02-01
影响因子：
0.7
作者：
Chen Peng-fei;Tremblay, Nicolas;Li Bao-guo
通讯作者：
Li Bao-guo
影响因子：
13.5
作者：
De Castro, A. I.;Ehsani, R.;Abdulridha, J.
通讯作者：
Abdulridha, J.
利用原位高光谱数据冠层植被指数评估白粉病胁迫下冬小麦植株水分状况
影响因子：
5.6
作者：
Feng W;Qi S;Heng Y;Zhou Y;Wu Y;Liu W;He L;Li X
通讯作者：
Li X