Enhancing wheat Fusarium head blight detection using rotation Yolo wheat detection network and simple spatial attention network

Abstract

The detection of Fusarium head blight (FHB), a destructive disease in wheat, can be performed through digit imaging. To improve detection accuracy and overcome challenges related to accurate labelling and detection efficacy, this study introduced two new networks: the Rotation Yolo Wheat Detection (RYWD) network and the Simple Spatial Attention (SSA) network. The RYWD network, utilizing the Yolo structure, served as a novel rotation detector capable of detecting wheat head images with detection boxes of arbitrary orientations. Angle prediction performance was enhanced by employing gray coding labels for angle encoding. Additionally, the SSA network, an unsupervised segmentation network, incorporated a spatial attention module and a spatial continuity loss to extract wheat features based on their spatial distribution. FHB detection was accomplished through HSV threshold segmentation and K-Means segmentation. The proposed method achieved an average accuracy of 94.66% in predicting the levels of FHB across two different years and locations. Comparatively, the proposed method outperformed previous research, exhibiting significant increases in both accuracy (11.8% increase) and precision (10.7% increase). These findings highlight the considerable improvement attainable through the integration of a rotation detector in crop disease detection, demonstrating its enhanced efficiency.