Optics Group, Physics Department, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain.
LPICM, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120, Palaiseau, France.
Sci Rep. 2022 Aug 30;12(1):14743. doi: 10.1038/s41598-022-19088-6.
This paper highlights the potential of using polarimetric methods for the inspection of plant diseased tissues. We show how depolarizing observables are a suitable tool for the accurate discrimination between healthy and diseased tissues due to the pathogen infection of plant samples. The analysis is conducted on a set of different plant specimens showing various disease symptoms and infection stages. By means of a complete image Mueller polarimeter, we measure the experimental Mueller matrices of the samples, from which we calculate a set of metrics analyzing the depolarization content of the inspected leaves. From calculated metrics, we demonstrate, in a qualitative and quantitative way, how depolarizing information of vegetal tissues leads to the enhancement of image contrast between healthy and diseased tissues, as well as to the revelation of wounded regions which cannot be detected by means of regular visual inspections. Moreover, we also propose a pseudo-colored image method, based on the depolarizing metrics, capable to further enhance the visual image contrast between healthy and diseased regions in plants. The ability of proposed methods to characterize plant diseases (even at early stages of infection) may be of interest for preventing yield losses due to different plant pathogens.
本文重点介绍了偏振方法在植物病变组织检测中的应用潜力。我们展示了由于植物样本受到病原体感染，去极化可观察量如何成为准确区分健康组织和病变组织的合适工具。该分析基于一组显示不同疾病症状和感染阶段的不同植物标本进行。通过使用完整的图像 Mueller 偏振计，我们测量了样本的实验 Mueller 矩阵，从中我们计算了一组度量值，用于分析所检查叶片的去极化含量。从计算出的度量值中，我们以定性和定量的方式展示了植物组织的去极化信息如何增强健康组织和病变组织之间的图像对比度，以及揭示常规目视检查无法检测到的受伤区域。此外，我们还提出了一种基于去极化度量的伪彩色图像方法，能够进一步增强植物中健康和病变区域之间的视觉图像对比度。所提出的方法对植物疾病进行特征描述的能力（即使在感染的早期阶段）可能有助于防止因不同植物病原体而导致的产量损失。
