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Segmentation of wheat scab fungus spores based on CRF_ResUNet plus

文献类型： 外文期刊

作者： Zhang, Dongyan 1 ; Zhang, Wenhao 1 ; Cheng, Tao 1 ; Lei, Yu 1 ; Qiao, Hongbo 3 ; Guo, Wei 3 ; Yang, Xue 4 ; Gu, Chunyan 1 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

2.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applica, Hefei 230601, Peoples R China

3.Henan Agr Univ, Sci Coll Informat & Management, Zhengzhou 450000, Peoples R China

4.Anhui Acad Agr Sci, Inst Plant Protect & Agroprod Safety, Hefei 230031, Peoples R China

关键词： Spore segmentation; UNet plus plus; Residual blocks; Fully connected conditional random fields; Wheat scab

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 216 卷

页码：

收录情况： SCI

摘要： Accurate segmentation of tiny spore pictures is a significant technique in computer-aided evaluation of wheat scab. A CRF_ResUNet++ spore segmentation model using UNet++ as the fundamental framework is proposed to address the issues of uneven brightness, low spore-background contrast, and spore adhesion in microscopically collected Fusarium spore images. ResNet and fully connected conditional random fields (CRF) are combined in this model to address these issues. First, a residual block ResNet is introduced into UNet++ to enhance the propagation of features and extract more spore detail information. Next, the spore image is initially segmented using UNet++ with an encoder-decoder structure. Finally, postprocessing is carried out using a fully connected conditional random field model to obtain more precise edges and complete spore regions. The laboratory collection dataset test results revealed that the F1-score and MIoU reached 0.943 and 0.925, respectively, with an average detection Precision improvement of 3.2 % over the original UNet++ model. In comparison to existing models, the suggested model can segment spore pictures in challenging circumstances, such as when they contain independent and inter-adherent spores, with excellent computer vision processing capabilities effects and objective evaluation metric segmentation effects. In conclusion, the method in this study can reliably detect and segment adherent wheat scab spores in complex backgrounds, which offers critical technical support for automatic detection of adherent wheat scab spores and early spore concentration prediction in complex environments in the field.

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