Abstract:

Timely detection of the severity of Fusarium head blight in the field and taking corresponding prevention and control measures based on the severity of the disease can improve the quality of wheat production. The current methods for identifying the severity of wheat Fusarium head blight are mostly based on identifying one or several wheat ears, which is not suitable for field investigations due to its low efficiency. To address this issue, the study proposes an efficient and accurate method for identifying the severity of wheat Fusarium head blight in the field. By introducing CBAM attention mechanism to improve the performance of YOLOv8m-seg model. Using the improved YOLOv8m-seg model to segment wheat ear instances in the collected distant images, and then using non target suppression method to cut individual wheat ear. Then, using the improved YOLOv8m-seg model to segment diseased and healthy spikelets in each wheat ear, the severity of Fusarium head blight in each wheat ear is calculated based on the number of diseased and healthy spikelets. To verify the effectiveness of the method proposed in this article, two datasets were constructed for testing, namely dateset of wheat ear (D-WE) and dateset of wheat spikelet (D-WS). The experimental results show that YOLOv8m-seg has better overall performance than YOLOv8n-seg, YOLOv8s-seg, YOLOv8l-seg, and YOLOv8x-seg on two datasets. The model that introduces CBAM is superior to the model that introduces SE, ECA, and CA attention mechanisms. Compared with the original model, the mean average precision of the improved YOLOv8m-seg model has increased by 0.9 percentage points and 1.2 percentage points on two datasets, respectively. The severity recognition method for Fusarium head blight proposed in this study has improved the severity accuracy by 38.4 percentage points, 6.2 percentage points, and 2.4 percentage points compared to the other three recognition methods. After deploying the improved YOLOv8m-seg model through TensorRT inference framework, the total algorithm time consumed is only 1/7 of the original. Finally, this study conducted a investigation on the severity of wheat Fusarium head blight in three locations based on AR glasses. The results showed that the average counting accuracy of intelligent identification of wheat Fusarium head blight based on AR glasses was as high as 0.953, and the investigation time is one-third of the manual investigation time. This fully demonstrates the effectiveness of the proposed method and lays a good foundation for intelligent field investigation of wheat Fusarium head blight.

Key words: Fusarium head blight, CNN, YOLOv8, attention mechanism, AR glasses