Shu Xin, Ding Jie, Wang Wenyu, Jiao Yuxuan, Wu Yunzhi
School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel). 2025 Aug 14;25(16):5058. doi: 10.3390/s25165058.
Accurate segmentation of pear leaf diseases is paramount for enhancing diagnostic precision and optimizing agricultural disease management. However, variations in disease color, texture, and morphology, coupled with changes in lighting conditions and gradual disease progression, pose significant challenges. To address these issues, we propose EBMA-Net, an edge-aware multi-scale network. EBMA-Net introduces a Multi-Dimensional Joint Attention Module (MDJA) that leverages atrous convolutions to capture lesion information at different scales, enhancing the model's receptive field and multi-scale processing capabilities. An Edge Feature Extraction Branch (EFFB) is also designed to extract and integrate edge features, guiding the network's focus toward edge information and reducing information redundancy. Experiments on a self-constructed pear leaf disease dataset demonstrate that EBMA-Net achieves a Mean Intersection over Union (MIoU) of 86.25%, Mean Pixel Accuracy (MPA) of 91.68%, and Dice coefficient of 92.43%, significantly outperforming comparison models. These results highlight EBMA-Net's effectiveness in precise pear leaf disease segmentation under complex conditions.
准确分割梨树叶病害对于提高诊断精度和优化农业病害管理至关重要。然而，病害颜色、纹理和形态的变化，加上光照条件的改变和病害的逐渐发展，带来了重大挑战。为了解决这些问题，我们提出了EBMA-Net，一种边缘感知多尺度网络。EBMA-Net引入了一个多维度联合注意力模块（MDJA），该模块利用空洞卷积在不同尺度上捕捉病变信息，增强了模型的感受野和多尺度处理能力。还设计了一个边缘特征提取分支（EFFB）来提取和整合边缘特征，引导网络关注边缘信息并减少信息冗余。在自建的梨树叶病害数据集上进行的实验表明，EBMA-Net的平均交并比（MIoU）达到86.25%，平均像素准确率（MPA）达到91.68%，Dice系数达到92.43%，显著优于比较模型。这些结果突出了EBMA-Net在复杂条件下精确分割梨树叶病害方面的有效性。
