Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs

• Published in: Drones (Basel) Vol. 8; no. 9; p. 445
• Main Authors: Dong, Ping, Wang, Ming, Li, Kuo, Qiao, Hongbo, Zhao, Yuyang, Bacao, Fernando, Shi, Lei, Guo, Wei, Si, Haiping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; Algorithms; Blight; CA attention mechanism; Crop diseases; Deep learning; disease identification; Diseases and pests; Drone aircraft; Feature extraction; Food quality; Fungal diseases of plants; Fusarium; fusarium head blight; Image enhancement; Image processing; Image quality; image segmentation; Machine learning; Methods; Modules; multispectral; Neural networks; Parameter estimation; Pathogens; Remote sensing; Semantic segmentation; Spatial data; Support vector machines; Unmanned aerial vehicles; Vegetation; Vegetation index; Wheat; Winter wheat; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8090445

Fusarium head blight (FHB), a severe ailment triggered by fungal pathogens, poses a considerable risk to both the yield and quality of winter wheat worldwide, underscoring the urgency for precise detection measures that can effectively mitigate and manage the spread of FHB. Addressing the limitations of current deep learning models in capturing detailed features from UAV imagery, this study proposes an advanced identification model for FHB in wheat based on multispectral imagery from UAVs. The model leverages the U2Net network as its baseline, incorporating the Coordinate Attention (CA) mechanism and the RFB-S (Receptive Field Block—Small) multi-scale feature extraction module. By integrating key spectral features from multispectral bands (SBs) and vegetation indices (VIs), the model enhances feature extraction capabilities and spatial information awareness. The CA mechanism is used to improve the model’s ability to express image features, while the RFB-S module increases the receptive field of convolutional layers, enhancing multi-scale spatial feature modeling. The results demonstrate that the improved U2Net model, termed U2Net-plus, achieves an identification accuracy of 91.73% for FHB in large-scale wheat fields, significantly outperforming the original model and other mainstream semantic segmentation models such as U-Net, SegNet, and DeepLabV3+. This method facilitates the rapid identification of large-scale FHB outbreaks in wheat, providing an effective approach for large-field wheat disease detection.