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

IF 4.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Plant Methods Pub Date : 2025-03-03 DOI:10.1186/s13007-025-01340-5

Hongda Li, Yue Zhao, Zeyang Bi, Peng Hao, Huarui Wu, Chunjiang Zhao

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引用次数: 0

Abstract

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 with actual measurements. The resulting goodness of fit R² values are 94.82%, 94.43%, and 86.45%, respectively. Verifying the effectiveness of this network in extracting the phenotypic traits of cabbage seedling roots.

Conclusions: The Swin-Unet + + framework developed in this study provides a new technique for the monitoring and analysis of cabbage root systems, ultimately leading to the development of an automated analysis platform that offers technical support for intelligent agriculture and efficient planting practices.

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Swin-Unet++：白菜幼苗根系表型参数分析研究。

背景：白菜作为一种重要的经济作物，其根系的生长状况直接影响白菜的整体健康和产量。为了监测白菜苗期根系的生长状况，本研究提出了一种新的网络架构swing - unet++。该架构集成了swing - transformer模块和残差网络，并使用注意机制取代传统的卷积操作进行特征提取。并采用残差概念融合不同层次的上下文信息，解决了白菜苗根细而网状的特征提取不足的问题。结果：与其他骨干高精度语义分割网络相比，SwinUnet + +的分割效果更优。结果表明，swwin - unet++在根系分割任务中的准确率高达98.19%，模型参数为60 M，平均响应时间为29.5 ms。与经典Unet网络相比，mIoU提高了1.08%，验证了swing - transformer和残差网络能够准确提取根的细粒度特征。对比不同语义分割后的图像通过轮廓定位根的位置时，win- unet + +的定位效果最好。在语义分割得到的根像素的基础上，计算出的最大根长度、延伸宽度和根厚度与实际测量值进行比较。得到的拟合优度R²值分别为94.82%、94.43%和86.45%。验证该网络在白菜幼苗根系表型性状提取中的有效性。结论：本研究开发的swan - unet++框架为白菜根系监测和分析提供了一种新技术，最终开发出一个自动化分析平台，为智能农业和高效种植提供技术支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Plant Methods 生物-植物科学

CiteScore

9.20

自引率

3.90%

发文量

121

审稿时长

2 months

期刊介绍： Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.