Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 22; p. 3811
• Main Authors: Liu, Linyi, Dong, Yingying, Huang, Wenjiang, Du, Xiaoping, Ma, Huiqin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2020
• Subjects: Agricultural production; Algorithms; Back propagation; Back propagation networks; Blight; BP neural network; Cameras; Chlorophyll; classification; Crop diseases; Destructive testing; Disease control; disease monitoring; Feature extraction; feature selection; Flowers & plants; Fusarium; Fusarium head blight; Hyperspectral imaging; Image acquisition; Infections; Medical imaging; Model accuracy; Monitoring; Morphology; Neural networks; Physiology; Remote sensing; Sensors; Simulated annealing; Spatial discrimination; Spatial resolution; Spectral bands; Triticum aestivum; Unmanned aerial vehicles; Vegetation; Wheat; wheat disease; Winter wheat; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12223811

The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.