This project provides an automated microscopy image analysis framework for pomegranate (Punica granatum L.) branches under cold stress, integrating deep learning, computer vision, and rule-based measurements. It supports quantitative analysis of paraffin-section and SEM images, including tissue thickness, vessel traits, and cambium continuity, providing standardized structural data for cold-tolerance research.
本项目面向寒冷胁迫下石榴（Punica granatum L.）枝条显微结构分析，构建了融合深度学习、计算机视觉与规则测量的自动化图像分析框架。该框架可对石蜡切片和扫描电镜（SEM）图像中的组织厚度、导管特征及形成层连续性等指标进行定量分析，为石榴抗寒性研究提供标准化的组织结构数据。