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结合高分辨率成像,深度学习和动态建模,在小麦树冠上分离疾病和衰老

Jonas Anderegg¹, Radek Zenkl¹, Achim Walter²

¹Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.

Plant phenomics (Washington, D.C.)

|June 26, 2023

概括

一种新的图像分析方法准确地追踪了作物中的绿叶腐烂,分离了化和亡. 这有助于理解压力对谷物填充和疾病耐药性的繁殖的影响.

科学领域:

农业科学农业科学
植物病理学植物病理学
图像分析图像分析

背景情况:

对于作物谷物填充来说,在解剖后保持绿叶面积至关重要.
植物衰老和压力因素 (生物/无生物) 加快了绿色的衰变,影响了产量.
量化压力引起的与与年龄相关的衰老是具有挑战性的.

研究的目的:

开发一种图像处理方法,用于在作物中区分化和亡.
为了单独监测绿色衰变动态和压力贡献.
为了使得对压力耐受性进行遗传研究的高通量表型化.

主要方法:

利用深度学习模型 (语义细分) 进行植被分析.
使用生成对抗网络 (GAN) 来创建半合成的培训数据.
应用图像时间序列分析,随着时间的推移跟踪化和亡.

主要成果:

开发了一种方法,单独量化化和枯在耳朵和芽.
基于图像的绿色衰变估计与传统评分有很强的相关性 (r ≈ 0.9).
观察到与叶子疾病水平相关的明显的绿色衰变模式,如Septoria tritici blotch.

结论:

分离化和亡可以准确量化压力和衰老的影响.
这种方法有助于研究生物压力和生理衰老之间的相互作用.
高通量方法支持对作物疾病耐药性和耐受性的遗传研究.

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