Abstract
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.
Highlights
IntroductionMultiple crop diseases and insect pests have emerged, with considerable impacts on yield and productivity following local outbreaks
Received: 11 November 2021In recent years, multiple crop diseases and insect pests have emerged, with considerable impacts on yield and productivity following local outbreaks
Previous literature has confirmed the importance of reflectance spectrum data in crop disease monitoring and its application prospects
Summary
Multiple crop diseases and insect pests have emerged, with considerable impacts on yield and productivity following local outbreaks. According to the United Nations Food and Agriculture Organization (FAO), 20%–40% of crops globally are damaged by disease and insect pests annually [1]. Powdery mildew is the major wheat disease; it causes considerable yield reductions or even no harvest, posing a major threat to wheat production and global food security. Water, and other biochemical components in plant tissues would inevitably yield diverse absorption and reflectance characteristics on the plant reflectance spectrum curve, which provides a theoretical basis and facilitates the real-time monitoring of wheat diseases using remote sensing technologies [2]. With continuous advancements in remote sensing technologies, numerous scholars have applied technologies to monitor wheat diseases. Different crops, varieties, and diseases exhibit diverse spectral characteristics, which
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