School of Information Engineering, Xinjiang University of Technology, Xinjiang, 843100, China.
BMC Bioinformatics. 2024 Aug 8;25(1):262. doi: 10.1186/s12859-024-05890-8.
In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction.
An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas.
The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.
在复杂的农业环境中，阴影、叶片碎片和光照不均匀等因素会影响黄瓜病害检测中叶片分割模型的性能。此外，背景和病变区域之间的像素比例失衡也会影响病变提取的准确性。
本文提出了一种原始的图像分割框架，即 LS-ASPP 模型，该模型采用两阶段 Atrous Spatial Pyramid Pooling (ASPP) 方法，并结合自适应损失来解决这些挑战。Leaf-ASPP 阶段采用注意力模块和残差结构来捕获多尺度语义信息，并增强边缘感知，从而能够从复杂背景中精确提取叶片轮廓。在 Spot-ASPP 阶段，我们调整了 ASPP 的扩张率，并引入了卷积注意力模块（CABM）来准确分割病变区域。
LS-ASPP 模型在复杂条件下的语义分割准确性方面表现出了改进，为精确的黄瓜病变分割提供了稳健的解决方案。通过关注具有挑战性的像素并适应农业图像分析的特定要求，我们的框架有可能提高病害检测的准确性，并有助于及时做出有效的作物管理决策。
