Sinusoidally modulated fluorescence imaging for stress detection in plants

作物植物受到多种环境胁迫，这些胁迫通过影响光合性能来限制其生产力。尽管经过多年的选择性育种，作物并没有最佳地适应农业环境，这一问题因全球气候变化而加剧。再加上病虫害的影响越来越大，干旱、洪水、盐碱化和极端温度等压力是农业生产力的主要限制因素。该项目的目的是开发一种新的，高度敏感的，非侵入性的方法来成像植物对胁迫的反应。这是一个高度跨学科的项目，汇集了植物生物学，工程和计算技术，以提供新的系统来衡量植物健康，从而提高粮食生产和安全，维持和改善可持续的全球粮食供应需要不断开发和评估改良作物品种，提供更高的产量，也能抵抗生物和气候变化相关的非生物胁迫。然而，评估新作物品种的表型是高度劳动密集型的，并且通常涉及农学家对植物性能的主观评估。植物“表型组学”旨在消除作物改良中的这一瓶颈，并在不断变化的环境中以高时间分辨率提供多种作物性状的定量数据。为了充分利用这种方法，需要简单易用的测量工具。这些工具的用途不仅限于植物育种，因为它们可以为农民监测作物或识别和早期干预的压力在精准农业。在这个建议中，我们将开发一个手持，便携式，非侵入性叶绿素荧光成像设备，使用振荡光探测微妙的，压力引起的光合功能的变化。当植物暴露在光下时，叶绿素吸收的一小部分光会以荧光的形式重新发射出来，这种荧光可以用来探测叶子的内部功能。当暴露于波动的光源时，光合机构的某些部分可以跟上这些变化，而其他部分则落后。这产生了一个复杂的输出，反映了叶片内部的光合作用，生理和代谢过程。本提案的目的是开发正弦调制荧光成像（SMFI）作为一种工具，早期，敏感和特异性检测植物胁迫。这种方法将提供一种新的分析方法，在检测亚致死植物胁迫方面比现有方法更灵敏和快速，为植物科学家，育种家和生产者提供新的功能。我们将开发一种用于SMFI成像的手持设备，使用它来检测，量化和区分特定的压力，将这些结果与植物代谢的基础模型相关联，然后使用机器学习/人工智能方法来优化采集协议和分析。该建议将填补植物胁迫测量、量化和识别方面的测量空白，并对学术和工业研究以及农业和园艺部门的应用产生影响。

Crop plants are subject to multiple environmental stresses that limit their productivity by impacting photosynthetic performance. Despite years of selective breeding, crops are not optimally adapted to the agricultural environment, a problem that is exacerbated by global climate change. Coupled with the increasing impact of pests and disease, stresses such as drought, flooding, salinity and temperature extremes are major limitations to agricultural productivity. The aim of this project is to develop a novel, highly sensitive, non-invasive approach for imaging plant responses to stress. It is a highly interdisciplinary project that brings together plant biology, engineering and computational techniques to deliver novel systems to measure plant health and thus improve food production and security.The maintenance and improvement of sustainable global food supplies requires continuous development and assessment of improved crop varieties that provide greater yields and are also resistant to biotic and climate-change related abiotic stresses. However, assessing the phenotype of new crop varieties is highly labour intensive and often involves subjective assessment of plant performance by agronomists. Plant 'phenomics' aims to remove this bottleneck in crop improvement and provide quantitative data at high temporal resolution of multiple crop traits in a changing environment. To fully exploit this approach, there is a need for simple-to-use measurement tools. The usefulness of such tools is not limited to plant breeding alone, as they allow crop monitoring for farmers or the identification and early intervention of stresses in precision agriculture.In this proposal, we will develop a hand-held, portable, non-invasive chlorophyll fluorescence imaging device that uses oscillating light to probe subtle, stress-induced changes in photosynthetic function. When plants are exposed to light, a small proportion of the lights absorbed by chlorophyll is re-emitted as fluorescence which can be used to probe the internal functions of the leaf. When exposed to a fluctuating light source, some parts of the photosynthetic apparatus can keep pace with these changes, whereas others lag behind. This generates a complex output reflecting the internal photosynthetic, physiological and metabolic processes in the leaf. The aim of this proposal is to develop sinusoidally modulated fluorescence imaging (SMFI) as a tool for the early, sensitive and specific detection of plant stress. This approach will deliver a new analysis approach that is more sensitive and rapid at detecting sub-lethal plant stress than existing approaches, providing new functionality for plant scientists, breeders and producers. We will develop a handheld device for SMFI imaging, use this to detect, quantify and discriminate between specific stresses, relate these results to underlying models of plant metabolism and then use machine learning/artificial intelligence approaches to optimise acquisition protocols and analysis. This proposal will fill a measurement gap in the measurement, quantification and identification of plant stress and have an impacts academic and industrial research, and application in the agricultural and horticultural sectors.