Lightweight U-Net-Based Method for Estimating the Severity of Wheat Fusarium Head Blight

Abstract

Wheat Fusarium head blight is one of the major diseases affecting the yield and quality of wheat. Accurate and rapid estimation of disease severity is crucial for implementing disease-resistant breeding and scientific management strategies. Traditional methods for estimating disease severity are complex and inefficient, often failing to provide accurate assessments under field conditions. Therefore, this paper proposes a method using a lightweight U-Net model for segmenting wheat spike disease spots to estimate disease severity. Firstly, the model employs MobileNetv3 as its backbone for feature extraction, significantly reducing the number of parameters and computational demand, thus enhancing segmentation efficiency. Secondly, the backbone network has been augmented with a lightweight Coordinate Attention (CA) module, which integrates lesion position information through channel attention and aggregates features across two spatial dimensions. This allows the model to capture long-range feature correlations and maintain positional information, effectively enhancing the segmentation of wheat spike disease spots while ensuring the model’s lightweight and efficient characteristics. Lastly, depthwise separable convolutions have been introduced in the decoder in place of standard convolutions, further reducing the model’s parameter count while maintaining performance. Experimental results show that the model’s segmentation Mean Intersection over Union (MIoU) reached 88.87%, surpassing the U-Net model by 3.49 percentage points, with a total parameter count of only 4.52 M, one-sixth of the original model. The improved model demonstrates its capability to segment individual wheat spike disease spots under field conditions and estimate the severity of infestation, providing technical support for disease identification research.