基于Gland-MSConNet模型的棉花叶片色素腺体分割与表型量化分析

Segmentation and Phenotypic Quantitative Analysis of Pigment Glands in Cotton Leaves Based on Gland-MSConNet Model

摘要:

棉酚是棉属植物特有的有毒物质，在农业和制药等领域具有重要的研究价值。色素腺体是棉酚的主要载体，其表型特征与棉酚含量密切相关，能够为棉酚含量评估提供参考。目前，色素腺体检测主要依赖人工分析，耗时且效率低。为此，本文提出了一种可在复杂背景下对棉花叶片色素腺体进行精准语义分割的模型Gland-MSConNet。该模型引入卷积双向Mamba模块和多尺度注意力模块，增强对多尺度特征的捕捉能力和复杂场景的适应性，并通过亚像素卷积实现高效上采样，避免局部细节丢失。结果表明，Gland-MSConNet模型分割平均交并比为89.83%，召回率为95.03%，与U-Net模型相比，分别提高4.66、5.78个百分点，具有较高的分割精度和鲁棒性。Gland-MSConNet模型为棉花叶片色素腺体分割与表型量化分析提供了有力的技术支持，为棉酚含量自动评估奠定了可靠基础。

Abstract:

Gossypol, a toxic compound unique to the Gossypium species, holds significant research value in agriculture and pharmaceuticals. Pigment glands, the primary carriers of gossypol, exhibit phenotypic traits that strongly correlate with gossypol content, making them effective indicators for its evaluation. However, the detection of pigment glands primarily relies on manual analysis, which is both time-consuming and inefficient. To address these limitations, Gland-MSConNet, a novel model designed for the accurate segmentation of cotton leaf pigment glands in complex backgrounds was proposed. The model integrated the convolutional bidirectional Mamba (ConBimamba) and multi-scale attention aggregation (MSAA) modules, significantly enhancing its ability to capture multi-scale features and improving robustness in challenging scenarios. Additionally, the incorporation of sub-pixel convolution facilitated efficient upsampling, preserving fine-grained details. Experimental results demonstrated that Gland-MSConNet achieved a segmentation mIoU of 89.83% and a recall rate of 95.03%, representing improvements of 4.66 percentage points and 5.78 percentage points, respectively, over the U-Net model. These findings highlighted the model’s high segmentation accuracy and robustness. The Gland-MSConNet model provided a strong technical foundation for the segmentation and phenotypic quantification of cotton leaf pigment glands, offering a reliable basis for the automated evaluation of gossypol content.