Automated 3D wheat tissue analysis using x-ray CT and deep learning

Abstract

Understanding wheat grain internal structures is critical for improving quality, pest resistance, and breeding efficiency. While X-ray computed tomography (CT) enables non-destructive 3D imaging, existing segmentation methods rely on manual intervention, introducing inefficiency and subjectivity. This study introduces the Residual Depthwise Separable Convolution and Vision Mamba U-Net (RDVM-UNet), an automated framework combining Depthwise Separable Convolution (DSConv) for efficient local feature extraction and Vision Mamba for global contextual modeling. Trained over 200 iterations, the model achieved a mean Intersection over Union (mIoU) of 95.4 % in segmenting wheat tissues (epidermis, embryo, endosperm). Validation across 10 varieties demonstrated robust generalizability (mIoU is 94.78 %) and rapid processing (9.65 s/grain). The framework generated 3D reconstructions, enabling precise quantification of morphological parameters (volume, surface area) critical for analyzing genetic-environmental-morphological relationships. By establishing a non-destructive, high-throughput pipeline, this work advances precision breeding, functional genomics, and trait optimization in cereal crops. RDVM-UNet bridges computational imaging and agricultural science, offering scalable solutions for crop phenotyping and quality enhancement.

Plant phenotyping relevance

X線CT画像から小麦組織を自動分割・3D再構成し、形態形質を定量化する深層学習パイプラインの開発と品種横断検証が中心であり、植物表現型計測法に該当する。

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