CornViT用三阶段模型自动分析玉米籽粒，精度超人工判断。
CornViT: A Multi-Stage Convolutional Vision Transformer Framework for Hierarchical Corn Kernel Analysis
- 分三阶段的卷积视觉变压器，模拟人眼逐级判断籽粒品质。
- 纯度、形态、胚芽朝向识别准确率分别达93.76%、94.11%、91.12%。
- 适合种子质检、育种研究者使用，提供可解释的网页工具。
精准分级玉米籽粒对种子认证、定向播种和育种至关重要，但目前仍依赖人工。本文提出CornViT，一种三阶段卷积视觉变压器（CvT）框架，模拟人类种子分析师的层级推理过程，实现单籽粒评估。三个串联的CvT-13分类器处理384x384 RGB图像：第一阶段区分纯与不纯籽粒；第二阶段将纯籽粒分为扁平与圆形；第三阶段判断纯扁平籽粒的胚芽朝向（上/下）。基于公开玉米种子图像数据集，手动重标注并筛选构建三个阶段专用数据集：纯度数据集7265个籽粒，形态数据集3859个纯籽粒，胚芽朝向数据集1960个纯扁平籽粒，均作为基准发布。在相同训练条件下，仅微调ImageNet-22k预训练的CvT-13骨干网络，测试准确率分别达到93.76%（纯度）、94.11%（形态）、91.12%（胚芽朝向）。相同条件下，ResNet-50准确率仅为76.56%~81.02%，DenseNet-121为86.56%~89.38%。结果表明卷积增强的自注意力机制在籽粒分析中具有优势。为促进应用，我们部署了基于Flask的Web应用，支持分阶段推理，并通过浏览器界面展示可解释输出。CornViT框架、标注数据集及网页工具共同构成可落地的自动化玉米籽粒质量评估方案。源代码与数据已公开。
原文摘要 · Abstract (English)
Accurate grading of corn kernels is critical for seed certification, directional seeding, and breeding, yet it is still predominantly performed by manual inspection. This work introduces CornViT, a three-stage Convolutional Vision Transformer (CvT) framework that emulates the hierarchical reasoning of human seed analysts for single-kernel evaluation. Three sequential CvT-13 classifiers operate on 384x384 RGB images: Stage 1 distinguishes pure from impure kernels; Stage 2 categorizes pure kernels into flat and round morphologies; and Stage 3 determines the embryo orientation (up vs. down) for pure, flat kernels. Starting from a public corn seed image collection, we manually relabeled and filtered images to construct three stage-specific datasets: 7265 kernels for purity, 3859 pure kernels for morphology, and 1960 pure-flat kernels for embryo orientation, all released as benchmarks. Head-only fine-tuning of ImageNet-22k pretrained CvT-13 backbones yields test accuracies of 93.76% for purity, 94.11% for shape, and 91.12% for embryo-orientation detection. Under identical training conditions, ResNet-50 reaches only 76.56 to 81.02 percent, whereas DenseNet-121 attains 86.56 to 89.38 percent accuracy. These results highlight the advantages of convolution-augmented self-attention for kernel analysis. To facilitate adoption, we deploy CornViT in a Flask-based web application that performs stage-wise inference and exposes interpretable outputs through a browser interface. Together, the CornViT framework, curated datasets, and web application provide a deployable solution for automated corn kernel quality assessment in seed quality workflows. Source code and data are publicly available.
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