交互式双分支特征融合的草莓病害程度快速诊断方法

针对现有草莓病害程度诊断方法存在识别精度低、参数量大、推理时间长等问题，提出了一种基于交互式双分支特征融合的草莓病害程度快速诊断方法。该方法首先以短程密集连接模块为基础，构建一种轻量化的交互式双分支特征融合网络(Interactive bilateral feature fusion network, IBFFNet),用于提取图像的语义特征和细节特征。然后，通过注意力简化的金字塔池化模块获取上下文分支中的多尺度语义特征，利用边缘增强模块丰富空间分支中的边缘细节特征。最后，融合多尺度语义特征和空间细节特征，实现病斑和叶片区域的精确分割。在草莓叶部病害程度数据集上的实验结果显示，IBFFNet2＿Seg的平均交并比达到77.8%,在单张NVIDIA GTX1050显卡上处理速度可达40.6 f/s,满足实际应用中对算法实时性和分割精度的要求。此外，在测试集上IBFFNet2＿Seg预测病害程度与真实程度的决定系数R²为0.98,说明该模型可以准确预测草莓病害严重程度。本研究可为草莓病害精准防治提供可靠的技术支撑。

Interactive Bilateral Feature Fusion Network for Real-time Strawberry Disease Diagnosis

Accurately identifying the severity of strawberry leaf disease is essential for precise disease control. However, methods based on image classification had a rough division of disease severity and fuzzy classification boundary, while methods based on semantic segmentation had high computational costs and long inference time. To address these problems, a real-time strawberry disease diagnosis method was proposed based on interactive bilateral feature fusion network (IBFFNet). The IBFFNet was a lightweight model containing a context path and a spatial path to extract semantic and detail features from the input image, respectively. Furthermore, an attention spatial pyramid pooling module was constructed to extract multi scale semantic features from the context path, and an edge enhancement module was designed to enrich edge detail information in the spatial path. Finally, the multi scale semantic feature and detail information were aggregated for precise leaf and lesion area segmentation. The percentage of lesions in the leaf area was the estimated severity. The method achieved a promising trade-off between accuracy and speed on the strawberry leaf disease diagnosis dataset. On the strawberry leaf disease diagnosis dataset, the mIoU of IBFFNet2_Seg was 77.8% with 40.6f/s on a single NVIDIA GTX1050. In the test set, an R2 value (coefficient of determination) of 0.98 was achieved, which denoted that the IBFFNet2_Seg could accurately predict the severity of the three diseases. This study paved the way for the precise control of strawberry disease.