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Peanut is an important oilseed crop in China, with significant differences among varieties in growth characteristics, yield potential, and stress resistance. The reticulation pattern on peanut pods, characterized by distinct varietal specificity in morphology, density, and distribution, serves as a key phenotypic indicator for DUS testing. However, existing studies have underutilized this trait. To address this, a U-Net based framework for peanut reticulation segmentation and multimodal feature fusion for variety identification was proposed. The U-Net model achieved outstanding performance in segmenting reticulation patterns through 13 peanut varieties, with a mean intersection over union of 75.9% and accuracy of 89.2%, significantly surpassing existing baseline models. Furthermore, 16 PCA-reduced reticulation features were combined with morphological and color features to construct a multimodal dataset. Using the support vector machine classifier, the framework achieved a classification accuracy of 90.15%, representing 4.4% improvement over combinations of texture, morphology, and color features. This study is the first to confirm the validity of peanut reticulation as a DUS testing trait, overcoming limitations of traditional morphological analysis. The proposed method provides an interpretable approach for peanut phenomics research and holds significant value for advancing precision breeding and germplasm conservation.
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Basic Information:
DOI：10.14001/j.issn.1002-4093.2026.01.003
China Classification Code:TP391.41;S565.2
Citation Information:
[1]GONG Xiuyi,ZONG Ziyan,FU Huayu ,et al.Peanut Reticulation Segmentation and Variety Identification Based on U-Net[J].Journal of Peanut Science,2026,55(01):23-33.DOI:10.14001/j.issn.1002-4093.2026.01.003.
Fund Information:
山东省重点研发计划(2021LZGC026-05,2021TZXD003-003,2024LZGC006,2024TZXD037); 中央引导地方发展专项(23139-zyyd-nsh,22134-zyyd-nsh); 山东省科技型中小企业提升工程项目(2022TSGC1114,2021TSGC1016); 山东省泰山学者工程专项(2021-216); 农业农村部神农英才计划(202302186)