Volume 3,Issue 7
小麦赤霉病表型鉴定技术的研究进展与展望
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。
[1]McMullen M, Bergstrom G, De Wolf E, et al. A unified effort to fight an enemy of wheat and barley: Fusarium head blight[J]. Plant disease, 2012, 96(12): 1712-1728.
[2]Parry D W, Jenkinson P, McLeod L. Fusarium ear blight (scab) in small grain cereals—a review[J]. Plant pathology, 1995, 44(2): 207-238.
[3]Bottalico A, Perrone G. Toxigenic Fusarium species and mycotoxins associated with head blight in small-grain cereals in Europe[J]. European journal of plant pathology, 2002, 108(7): 611-624.
[4]Zhang N, Yang G, Pan Y, et al. A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades[J]. Remote Sensing, 2020, 12(19): 3188.
[5]Zhang H, Huang L, Huang W, et al. Detection of wheat Fusarium head blight using UAV-based spectral and image feature fusion[J]. Frontiers in plant science, 2022, 13: 1004427
[6]Thapa S, Gill H S, Halder J, et al. Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight–related traits in winter wheat[J]. The Plant Genome, 2024, 17(3): e20470.
[7]Ackerman A J, Holmes R, Gaskins E, et al. Evaluation of methods for measuring fusarium-damaged kernels of wheat[J]. Agronomy, 2022, 12(2): 532.
[8]Mahlein A K. Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping[J]. Plant disease, 2016, 100(2): 241-251.
[9]Singh A, Ganapathysubramanian B, Singh A K, et al. Machine learning for high-throughput stress phenotyping in plants[J]. Trends in plant science, 2016, 21(2): 110-124.
[10] 张正华 , 吴宇 , 金志琦 . 基于 MHSA-YOLOv7的小麦赤霉病感染率检测 [J]. 无线电工程 , 2024, 54(1): 71-77.
[11] 朱文静 , 戴世元 , 冯展康 , 等 . 基于垄间背景剔除优化小麦赤霉病遥感监测精度 [J]. 农业工程学报 , 2024, 40(7): 219-229.
[12] 李国强 , 贾海燕 , 孔忠新 , 等 . 小麦抗赤霉病的遗传基础与育种应用 [J]. 生命科学 , 2025, 37(5): 548-557.
[13] 基于 YOLOv8-FECA 的密集场景下小麦赤霉病孢子目标检测 [J]. 农业工程学报 , 2024 (DOI:10.11975/j.issn.1002-6819.202406149).
[14] 基于堆栈稀疏自编码器的小麦赤霉病高光谱遥感检测 [J]. 作物学报 , 2023, 49(10): 2785-2796.
[15]Bao W, et al. Detection of Fusarium head blight in wheat using UAV remote sensing based on parallel channel space attention[J]. Computers and Electronics in Agriculture, 2024, 218: 108663. (PCSA-YOLO)
[16]Yin N, et al. ImgR-YOLO: a detection network for wheat Fusarium head blight[J]. International Journal of Remote Sensing, 2025 (in press).
[17]Feng G, et al. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective[J]. Plants, 2024, 13(13): 1722.
[18]Lin YF, et al. Self-supervised Fusarium Head Blight Detection with Hyperspectral Image and Feature Mining[EB/OL]. arXiv:2409.00395, 2024.