TACSTechnology and Application of Computer Science2998-89262998-8934Art and Technology10.61369/TACS.2026060031Article小麦赤霉病表型鉴定技术的研究进展与展望https://artdesignp.com/journal/TACS/3/6/10.61369/TACS.2026060031姚佳怡2026362026-03-28小麦赤霉病是影响产量与品质的关键病害，其表型鉴定水平直接决定抗病育种效率。本文围绕人工调查、可见光成像与深度学习（CNN、YOLO、Vision Transformer）、迁移学习与数据增强、多光谱与高光谱、无人机遥感及多源融合方法，系统梳理赤霉病从接种到成熟期的关键观测指标、采集流程和建模策略，对比不同技术路线在精度、通量与成本上的差异，给出病情指数、F1 值、mAP 等常用评价公式，构建危害规模、方法性能和病程动态的图表化表达。最后从标准化数据集、跨域自适应、可解释 AI、表型 - 基因组耦合和边缘部署五个方面提出展望，为赤霉病数字化表型平台建设与抗病育种应用提供参考。小麦赤霉病检测, 高通量表型, 高光谱成像, 卷积神经网络, 农业计算机视觉, YOLO, 无人机遥感

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