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Segmentation and Phenotypic Quantitative Analysis of Pigment Glands in Cotton Leaves Based on Gland-MSConNet Model
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Limin SHAO1, 2, Yuhong GENG1, Yaxuan XU1, Guoning WANG3, Geng YAN1, Yi ZHANG1
Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12) : 510 - 521
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Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12): 510-521
• Agricultural Informatization Engineering •
Segmentation and Phenotypic Quantitative Analysis of Pigment Glands in Cotton Leaves Based on Gland-MSConNet Model
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Limin SHAO1, 2, Yuhong GENG1, Yaxuan XU1, Guoning WANG3, Geng YAN1, Yi ZHANG1
Affiliations
  • 1.College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China
  • 2.Technology Innovation Center of Intelligent Agricultural Equipment of Hebei Province, Baoding 071001, China
  • 3.College of Agronomy, Hebei Agricultural University, Baoding 071001, China
Published: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.047
Outline
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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.

cotton leaf  /  pigment gland  /  gossypol  /  semantic segmentation
邵利敏, 耿玉红, 徐雅轩, 王国宁, 闫庚, 张怡. 基于Gland-MSConNet模型的棉花叶片色素腺体分割与表型量化分析. 农业机械学报, 2025 , 56 (12) : 510 -521 . DOI: 10.6041/j.issn.1000-1298.2025.12.047
Limin SHAO, Yuhong GENG, Yaxuan XU, Guoning WANG, Geng YAN, Yi ZHANG. Segmentation and Phenotypic Quantitative Analysis of Pigment Glands in Cotton Leaves Based on Gland-MSConNet Model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 510 -521 . DOI: 10.6041/j.issn.1000-1298.2025.12.047
Year 2025 volume 56 Issue 12
154
68
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Article Info
doi: 10.6041/j.issn.1000-1298.2025.12.047
  • Receive Date:2025-05-26
  • Online Date:2026-09-30
  • Published:2025-12-25
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  • Received:2025-05-26
  • Revised:2025-07-24
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Affiliations
    1.College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding 071001, China
    2.Technology Innovation Center of Intelligent Agricultural Equipment of Hebei Province, Baoding 071001, China
    3.College of Agronomy, Hebei Agricultural University, Baoding 071001, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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