基于U-Net的花生网纹分割与品种识别OA

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.