Methods to optimize optical sensing of biotic plant stress - combined effects of hyperspectral imaging at night and spatial binning.

IF 4.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Christian Nansen, Patrice J Savi, Anil Mantri
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Abstract

In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean (Glycine max) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites (Tetranychus urticae). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.

优化植物生物胁迫光学传感的方法--夜间高光谱成像和空间分档的综合效应。
在植物时空监测方面,光学传感(包括高光谱成像)正被用于无创检测和诊断植物对非生物和生物胁迫的反应。及早、准确地检测和诊断压力因素是关键目标。光学传感数据的辐射重复性水平与准确检测和诊断生物压力的能力成反比。因此,可以说在植物研究和作物生产中广泛采用光学传感技术的一个最重要的前沿领域和挑战就是如何最大限度地提高辐射重复性。在本研究中,我们从大豆(Glycine max)和有/无双斑蜘蛛螨(Tetranychus urticae)实验侵染的鹅掌楸精灵天鹅绒红(Solenostemon scutellarioides)植物上获取了中午和午夜的高光谱光学传感数据。我们探讨了与优化辐射测量重复性有关的三个问题:(1) 基于反射率的植物反应是否会受到光学传感时间的影响?(2) 如果是,植物对二斑蛛螨(生物胁迫)的反应在午夜和中午是否更明显? (3) 高光谱成像数据的空间分档(平滑化)是否能增强生物胁迫的检测?这项研究的结果为辐射测量重复性的计算提供了启示。研究结果有力地支持了以下观点,即获取光学传感数据来检测和描述植物对生物胁迫的反应,应在夜间进行。此外,结合午夜成像和空间分选,大豆和鹅掌楸的分类准确率分别提高了 29% 和 31%。本文讨论了这些发现的实际意义。研究结果几乎适用于所有光学传感应用,以检测和诊断植物在受控环境和室外作物生产系统中的非生物和生物胁迫反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Plant Methods
Plant Methods 生物-植物科学
CiteScore
9.20
自引率
3.90%
发文量
121
审稿时长
2 months
期刊介绍: Plant Methods is an open access, peer-reviewed, online journal for the plant research community that encompasses all aspects of technological innovation in the plant sciences. There is no doubt that we have entered an exciting new era in plant biology. The completion of the Arabidopsis genome sequence, and the rapid progress being made in other plant genomics projects are providing unparalleled opportunities for progress in all areas of plant science. Nevertheless, enormous challenges lie ahead if we are to understand the function of every gene in the genome, and how the individual parts work together to make the whole organism. Achieving these goals will require an unprecedented collaborative effort, combining high-throughput, system-wide technologies with more focused approaches that integrate traditional disciplines such as cell biology, biochemistry and molecular genetics. Technological innovation is probably the most important catalyst for progress in any scientific discipline. Plant Methods’ goal is to stimulate the development and adoption of new and improved techniques and research tools and, where appropriate, to promote consistency of methodologies for better integration of data from different laboratories.
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