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A Hierarchical Feature-Aware Model for Accurate Tomato Blight Disease Spot Detection: Unet with Vision Mamba and ConvNeXt Perspective

文献类型： 外文期刊

作者： Shi, Dongyuan 1 ; Li, Changhong 1 ; Shi, Hui 2 ; Liang, Longwei 1 ; Liu, Huiying 1 ; Diao, Ming 1 ;

作者机构： 1.Shihezi Univ, Coll Agron, Key Lab Special Fruits & Vegetables Cultivat Physi, Agr Coll, Shihezi 832003, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Meteorol Serv Ctr Urban Agr, Res Ctr Informat Technol, China Meteorol Adm,Minist Agr & Rural Affairs,Natl, Beijing 100097, Peoples R China

关键词： tomato blight; spot segmentation; Unet; parallel backbone; multi-scale feature fusion

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

页码：

收录情况： SCI

摘要： Tomato blight significantly threatened tomato yield and quality, making precise disease detection essential for modern agricultural practices. Traditional segmentation models often struggle with over-segmentation and missed segmentation, particularly in complex backgrounds and with diverse lesion morphologies. To address these challenges, we proposed Unet with Vision Mamba and ConvNeXt (VMC-Unet), an asymmetric segmentation model for quantitative analysis of tomato blight. Built on the Unet framework, VMC-Unet integrated a parallel feature-aware backbone combining ConvNeXt, Vision Mamba, and Atrous Spatial Pyramid Pooling (ASPP) modules to enhance spatial feature focusing and multi-scale information processing. During decoding, Vision Mamba was hierarchically embedded to accurately recover complex lesion morphologies through refined feature processing and efficient up-sampling. A joint loss function was designed to optimize the model's performance. Extensive experiments on both tomato epidemic and public datasets demonstrated VMC-Unet superior performance, achieving 97.82% pixel accuracy, 87.94% F1 score, and 86.75% mIoU. These results surpassed those of classical segmentation models, underscoring the effectiveness of VMC-Unet in mitigating over-segmentation and under-segmentation while maintaining high segmentation accuracy in complex backgrounds. The consistent performance of the model across various datasets further validated its robustness and generalization potential, highlighting its applicability in broader agricultural settings.

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