Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (4): 1-8.doi: 10.6040/j.issn.1672-3961.0.2024.232

• Special Issue for Deep Learning with Vision •    

Semantic segmentation method for potato common scab images based on multiscale feature fusion

WU Qiulan1, SHANG Suya1,2, ZHANG Jiahui3, SUN Shouxin1, ZHANG Feng1, ZHOU Bo3,4*, GAO Zheng3, SHI Wenchong3   

  1. WU Qiulan1, SHANG Suya1, 2, ZHANG Jiahui3, SUN Shouxin1, ZHANG Feng1, ZHOU Bo3, 4*, GAO Zheng3, SHI Wenchong3(1. School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, Shandong, China;
    2. Shandong Academy of Grape, Jinan 250100, Shandong, China;
    3. School of Life Sciences, Shandong Agricultural University, Taian 271018, Shandong, China;
    4. Eukaryote Biotechnology(Shandong)Co., Ltd., Taian 271000, Shandong, China
  • Published:2025-08-31

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Abstract: To precisely segment potato common scab lesions, a semantic segmentation model named MSFF-UNet was proposed. During the decoder's upward fusion process, feature enhancement was implemented through convolution and normalization operations to improve the differentiation of growth conditions in lesions of varying sizes. Additionally, multi-dimensional feature fusion capability was incorporated, where enhanced feature extraction from the decoder's high-level data was performed and subsequently fused with low-level data to capture semantic information of potatoes or common scab lesions at different scales. The results demonstrated that the improved semantic segmentation model achieved 93.90% precision, 93.51% mean class pixel accuracy, and 87.72% mean intersection over union, effectively enabling accurate segmentation of potatoes and common scab lesions.

Key words: semantic segmentation, feature fusion, UNet, potato, common scab

CLC Number: 

  • TP391
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