Swin-Unet++: a study on phenotypic parameter analysis of cabbage seedling roots

作者：Hongda Li, Yue Zhao, Z. B. Bi, Peng Hao, Huarui Wu, Chunjiang Zhao · 发表于：Plant Methods · 年份：2025 · DOI：10.1186/s13007-025-01340-5 · 被引用次数：8 · 研究领域：Plant nutrient uptake and metabolism、Smart Agriculture and AI、Plant Molecular Biology Research

BACKGROUND: As an important economic crop, the growth status of the root system of cabbage directly affects its overall health and yield. To monitor the root growth status of cabbage seedlings during their growth period, this study proposes a new network architecture called Swin-Unet++. This architecture integrates the Swin-Transformer module and residual networks and uses attention mechanisms to replace traditional convolution operations for feature extraction. It also adopts the residual concept to fuse contextual information from different levels, addressing the issue of insufficient feature extraction for the thin and mesh-like roots of cabbage seedlings. RESULTS: Compared with other backbone high-precision semantic segmentation networks, SwinUnet + + achieves superior segmentation results. The results show that the accuracy of Swin-Unet + + in root system segmentation tasks reached as high as 98.19%, with a model parameter of 60 M and an average response time of 29.5 ms. Compared with the classic Unet network, the mIoU increased by 1.08%, verifying that the Swin-Transformer and residual networks can accurately extract the fine-grained features of roots. Furthermore, when images after different semantic segmentations are compared to locate the root position through contours, Swin-Unet + + has the best positioning effect. On the basis of the root pixels obtained from semantic segmentation, the calculated maximum root length, extension width, and root thickness are compared...