花生学报2026，Vol.55Issue(1)：23-33,11.DOI:10.14001/j.issn.1002-4093.2026.01.003
基于U-Net的花生网纹分割与品种识别
Peanut Reticulation Segmentation and Variety Identification Based on U-Net
摘要
Abstract
Peanut is an important oilseed crop in China,with significant differences among varieties in growth charac-teristics,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,ex-isting 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 accura-cy 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 clas-sifier,the framework achieved a classification accuracy of 90.15％,representing 4.4％improvement over combinations of tex-ture,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.关键词
花生网纹/DUS性状/U-Net/图像分割/品种识别Key words
peanut reticulation/DUS traits/U-Net/image segmentation/variety identification分类
信息技术与安全科学引用本文复制引用
巩秀钇,踪姿艳,付华宇,张贺,纪翔,朱春雨,王聪,赵延伸,韩仲志..基于U-Net的花生网纹分割与品种识别[J].花生学报,2026,55(1):23-33,11.基金项目
山东省重点研发计划(2021LZGC026-05,2021TZXD003-003,2024LZGC006,2024TZXD037) （2021LZGC026-05,2021TZXD003-003,2024LZGC006,2024TZXD037）
中央引导地方发展专项(23139-zyyd-nsh,22134-zyyd-nsh) （23139-zyyd-nsh,22134-zyyd-nsh）
山东省科技型中小企业提升工程项目(2022TSGC1114,2021TSGC1016) （2022TSGC1114,2021TSGC1016）
山东省泰山学者工程专项(2021-216) （2021-216）
农业农村部神农英才计划(202302186) （202302186）
