Capturing crop adaptation to abiotic stress using image-based technologies

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.     

Opportunity and challenges of phenotyping plant salt tolerance

Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.     

Quantitative imaging using genetically encoded sensors for small molecules in plants

Quantitative imaging in live cells is a powerful method for monitoring the dynamics of biomolecules at an excellent spatio-temporal resolution. Such an approach, initially limited to a small number of substrates for which specific dyes were available, has become possible for a large number of biomolecules due to the development of genetically encoded, protein-based sensors. These sensors, which can be introduced into live cells through a transgenic approach, offer the benefits of quantitative imaging, with an extra advantage of non-invasiveness. In the past decade there has been a drastic expansion in the number of biomolecules for which genetically encoded sensors are available, and the functional properties of existing sensors are being improved at a dramatic pace. A number of technical improvements have now made the application of genetically encoded sensors in plants rather straightforward, and some of the sensors such as calcium indicator proteins have become standard analytical tools in many plant laboratories. The use of a handful of probes has already revealed an amazing specificity of cellular biomolecule dynamics in plants, which leads us to believe that there are many more discoveries to be made using genetically encoded sensors. In this short review, we will summarize the progress made in the past 15?years in the development in genetically encoded sensors, and highlight significant discoveries made in plant biology.     

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High‐throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis‐related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio‐engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype‐to‐gene identification pipeline.     

Multi-sensor plant imaging: Towards the development of a stress-catalogue

Agricultural production is limited by a wide range of abiotic (e.g. drought, waterlogging) and biotic (pests, diseases and weeds) stresses. The impact of these stresses can be minimized by appropriate management actions such as irrigation or chemical pesticide application. However, further optimization requires the ability to diagnose and quantify the different stresses at an early stage. Particularly valuable information of plant stress responses is provided by plant imaging, i.e. non-contact sensing with spatial resolving power: (i) thermal imaging, detecting changes in transpiration rate and (ii) fluorescence imaging monitoring alterations in photosynthesis and other physiological processes. These can be supplemented by conventional video imagery for study of growth. An efficient early warning system would need to discriminate between different stressors. Given the wide range of sensors, and the association of specific plant physiological responses with changes at particular wavelengths, this goal seems within reach. This is based on the organization of the individual sensor results in a matrix that identifies specific signatures for multiple stress types. In this report, we first review the diagnostic effectiveness of different individual imaging techniques and then extend this to the multi-sensor stress-identification approach.     

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.     

放牧对草原植物功能性状影响研究进展

植物功能性状的表达和植被环境适应性相关,植物功能性状之间的权衡变化体现了植物在放牧胁迫下资源的重新整合和获取.本文总结了放牧干扰下植物功能性状表达的差异性,着重将放牧干扰与植物功能性状相结合,介绍了植物功能性状的变异来源是植物遗传特征与环境过滤相互协调的结果,归纳了放牧对植物营养性状、繁殖性状的影响,以及植物可以通过调...     

植物功能性状对生态系统服务影响研究进展

全面认识和理解生态系统服务的形成机制是维持其持续供给的前提。植物功能性状直接参与多种生态系统过程, 影响生态系统服务供给, 探讨植物功能性状与生态系统服务的关系是揭示生态系统服务形成机制的重要途径。该文采用系统的文献综述方法, 分析了植物功能性状与生态系统服务关系的研究特点, 总结了影响不同生态系统服务的主要植物功能性状, 阐述了可能的影响途径。结果表明: 植物功能性状与生态系统服务关系研究以草地和森林等自然生态系统为主; 大部分研究集中在生态系统供给服务和支持服务, 包括生物量、净初级生产力、土壤肥力等; 根据植物功能性状对不同生态系统服务的影响程度, 植物功能性状可以聚类为土壤保持服务相关性状、水分循环相关性状、多功能相关性状、产品提供服务与养分循环相关性状以及授粉与生物控制服务相关性状; 并阐述了植物功能性状指标影响不同的生态系统服务途径。围绕植物功能性状对生态系统服务的影响, 今后尚需进一步探讨生态系统多功能性、植物功能性状相关性、气候变化和人类活动不确定性、时空尺度差异等因素对二者关系的影响。     

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.     

植物功能性状与环境和生态系统功能

植物性状反映了植物对生长环境的响应和适应,将环境、植物个体和生态系统结构、过程与功能联系起来(所谓的“植物功能性状”)。该文介绍了植物功能性状的分类体系,综述了国内外植物功能性状与气候(包括气温、降水、光照)、地理空间变异(包括地形地貌、生态梯度、海拔)、营养、干扰(包括火灾、放牧、生物入侵、土地利用)等环境因素,以及与生态系统功能之间关系的研究进展,探讨了全球变化(气候变化和CO₂浓度升高)对个体和群落植物功能性状的影响。植物功能性状的研究已经取得很多成果,并应用于全球变化、古植被恢复和古气候定量重建、环境监测与评价、生态保护和恢复等研究中,但大尺度、多生境因子下的植物功能性状研究仍有待于加强,同时需要改进性状的测量手段;我国的植物功能性状研究还需要更加明朗化和系统化。     

Genetically encoded fluorescent indicators for live cell pH imaging

Background

Intracellular pH underlies most cellular processes. There is emerging evidence of a pH-signaling role in plant cells and microorganisms. Dysregulation of pH is associated with human diseases, such as cancer and Alzheimer's disease.

Designs,applications, and limitations of genetically encoded fluorescent sensors to explore plant biology

The understanding of signaling and metabolic processes in multicellular organisms requires knowledge of the spatial dynamics of small molecules and the activities of enzymes, transporters, and other proteins in vivo, as well as biophysical parameters inside cells and across tissues. The cellular distribution of receptors, ligands, and activation state must be integrated with information about the cellular distribution of metabolites in relation to metabolic fluxes and signaling dynamics in order to achieve the promise of in vivo biochemistry. Genetically encoded sensors are engineered fluorescent proteins that have been developed for a wide range of small molecules, such as ions and metabolites, or to report biophysical processes, such as transmembrane voltage or tension. First steps have been taken to monitor the activity of transporters in vivo. Advancements in imaging technologies and specimen handling and stimulation have enabled researchers in plant sciences to implement sensor technologies in intact plants. Here, we provide a brief history of the development of genetically encoded sensors and an overview of the types of sensors available for quantifying and visualizing ion and metabolite distribution and dynamics. We further discuss the pros and cons of specific sensor designs, imaging systems, and sample manipulations, provide advice on the choice of technology, and give an outlook into future developments.

Different types of genetically encoded sensors in plants can be used to quantify and visualize ion and metabolite distributions and dynamics.     

pH biosensing in the plant apoplast—a focus on root cell elongation

The pH parameter of soil plays a key role for plant nutrition as it is affecting the availability of minerals and consequently determines plant growth. Although the mechanisms by which root perceive the external pH is still unknown, the impact of external pH on tissue growth has been widely studied especially in hypocotyl and root. Thanks to technological development of cell imaging and fluorescent sensors, we can now monitor pH in real time with at subcellular definition. In this focus, fluorescent dye-based, as well as genetically-encoded pH indicators are discussed especially with respect to their ability to monitor acidic pH in the context of primary root. The notion of apoplastic subdomains is discussed and suggestions are made to develop fluorescent indicators for pH values below 5.0.

The pH parameter of soil plays a key role for plant nutrition as it is affecting the availability of minerals and consequently determines plant growth. Although the mechanisms by which root perceive the external pH is still unknown, the impact of external pH on tissue growth has been widely studied especially in hypocotyl and root. Thanks to technological development of cell imaging and fluorescent sensors, we can now monitor pH in real time with at subcellular definition. In this focus, fluorescent dye-based, as well as genetically-encoded pH indicators are discussed especially with respect to their ability to monitor acidic pH in the context of primary root. The notion of apoplastic subdomains is discussed and suggestions are made to develop fluorescent indicators for pH values below 5.0.     

Are traits measured on pot grown plants representative of those in natural communities?

Question: The quantification of functional traits in natural communities can be difficult (e.g. root traits, RGR). Can functional traits measured on pot grown plants be reliably applied to natural communities? Alternatively, can below‐ground plant traits be predicted from above‐ground traits? Location: Southeastern Australia. Methods: We compared 17 shoot, root and whole‐plant morphological traits measured on 14 plant species in a native grassland community to those measured under two different pot conditions: unfertilised and fertilised. Results: The majority of trait values for pot grown plants differed to plants in the field, however, species ranking remained consistent for most leaf traits between the field and the two pot growing conditions. In contrast, species ranking was not consistent for most whole plant traits when comparing field plants to fertilised pot grown plants, providing a caution against the tendency to grow plants in controlled conditions at ‘optimal’ (high) resource levels. Moderate to strong correlations were found between below‐ground and above‐ground plant traits, including between root dry matter content and leaf dry matter content, and between specific root area and specific leaf area. Conclusions: The utility of pot grown plants to quantify traits for field plants is highly dependent on the selection of the growing conditions in the controlled environment. The consistency we observed between above‐ground and below‐ground trait strategies suggests that below‐ground traits may be predictable based on above‐ground traits, reducing the need to quantify root traits on cultured plants.     

Review of optical-based remote sensing for plant trait mapping

Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity.In this review, we first introduce RS concepts of light–vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/concentration, and 45% for leaf mass per area estimates.The potential of RS to map plant traits is particularly high when traits are related to leaf biochemistry, photosynthetic processes and canopy structure. There are also other plant traits, i.e. leaf chlorophyll content, water content and leaf area index, which can be retrieved from optical RS well and can be of importance for plant scientists.We underline the need that future assessments of ecosystem functioning using RS should require comprehensive and integrated measurements of various plant traits together with leaf and canopy spectral properties. By doing so, the interplay between plant structural, physiological, biochemical, phenological and spectral properties can be better understood.     

植物性状研究的机遇与挑战:从器官到群落

植物性状(Plant trait)或植物功能性状(Plant functional trait)通常是指植物对外界环境长期适应与进化后所表现出的可量度、且与生产力优化或环境适应等密切相关的属性。近几十年来,植物性状研究在性状-生产力、性状-养分、性状间相互关系、性状-群落结构维持等方面取得了卓越成就。然而,由于大多数性状调查都是以植物群落内优势种或亚优势种为对象,使其在探讨群落尺度的性状-功能关系、性状数据如何用于改进或优化模型、性状数据如何与遥感连接等问题时,存在空间尺度和量纲不匹配的极大挑战。为了破解上述难题,亟需发展新的、基于单位土地面积的群落性状(Community trait)概念体系、数据源和计算方法等,推动植物性状数据与快速发展的宏观生态学新技术(遥感、模型和通量观测等)相结合,既拓展了植物性状研究范畴,又可推动其更好地服务于区域生态环境问题的解决。所定义的群落性状(如叶片氮含量、磷含量、比叶面积、气孔密度、叶绿素含量等),是在充分考虑群落内所有物种的性状实测数据,再结合比叶面积、生物量异速生长方程和群落结构数据等,推导而成的基于单位土地面积的群落性状。受测试方法的影响,传统的直接算术平均法或相对生物量加权平均法所获得的群落水平的植物性状(如叶片氮含量g/kg或%),虽然可以有效地探讨群落结构维持机制,由于无法实现对群落性状在量纲上向单位土地面积转换,使它很难与模型和遥感数据相匹配。基于单位土地面积的群落性状,可在空间尺度匹配(或量纲匹配)的前提下实现个体水平测定的植物性状数据与生态模型和遥感观测相联系,更好地探讨区域尺度下自然生态系统结构和功能的关系及其对全球变化的响应与适应。同时,它也可更好地建立群落水平的性状-功能的定量关系(非物种水平),为更好地探讨自然群落结构维持机制和生产力优化机制提供了新思路。     

荒漠植物性状权衡策略及功能多样性研究进展

荒漠植物是干旱区具有独特功能性状与资源权衡表征的地带性植物。植物功能性状及其多样性格局与资源权衡策略对群落结构优化和生态系统功能改善起着关键作用。该综述主要从荒漠植物组织、器官功能性状特征、功能性状权衡策略、功能多样性组分及测度3个方面梳理了荒漠植物性状权衡策略与功能多样性研究的进展脉络：1）荒漠植物独特的根、茎、叶功能性状特征揭露了植被对环境变化的响应以及对生态系统功能的影响,基于植物功能性状的研究有助于解决许多生态学的关键性问题;2）作为植物功能性状之间存在的最普遍的联系,权衡策略是经过自然筛选后形成的性状组合,关键性状已经被发掘并创造性的提出了"经济谱"概念。荒漠植物研究过程中,应分析其根、茎、叶的特征属性筛选关键性状,着眼于关键性状间及整株植物性状间的权衡策略;3）功能多样性是影响生态系统运行和发挥作用的生物多样性的重要组成部分,荒漠植物功能多样性能预测和指示群落中物种对于荒漠生态系统功能发挥和过程变化的影响。功能多样性的组分可以从不同角度反映群落的生态位占据状况和资源利用程度,指数的选择要体现在群落内部物种的功能特征之间的差异程度,同时要考虑这些物种自身在群落内的优势程度。本研究为未来荒漠植物功能性状及多样性研究梳理了一些新的研究方向和内容,期望为荒漠植物生理生态学研究的选题和发展提供一些新的思路。     

High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement

Marker assisted plant breeding is a powerful technique for targeted crop improvement in horticulture and agriculture. It depends upon the correlation of desirable phenotypic characteristics with specific genetic markers. This can be determined by statistical models that relate the variation in the value of genetic markers to variation in phenotypic traits. It therefore depends upon the convergence of three technologies; the creation of genetically characterised (and thus marked) populations, high throughput screening procedures, and statistical procedures. While a large number of high throughput screening technologies are available, real-time screening techniques are usually based on some kind of imaging technologies, such as chlorophyll fluorescence imaging, that offers physiological data that are eminently suitable as a quantitative trait for genetic mapping.     

Relationships between species’ floral traits and pollinator visitation in a temperate grassland

Knowledge about plant–plant interactions for pollinator service at the plant community level is still scarce, although such interactions may be important to seed production and hence the population dynamics of individual plant species and the species compositions of communities. An important step towards a better understanding of pollination interactions at the community level is to assess if the variation in floral traits among plant species explain the variation in flower visitation frequency among those species. We investigated the relative importance of various floral traits for the visitation frequency of all insects, and bumblebees and flies separately, to plant species by measuring the visitation frequency to all insect-pollinated species in a community during an entire flowering season. Visitation frequency was identified to be strongly positive related to the visual display area and the date of peak flowering of plant species. Categorical variables, such as flower form and symmetry, were important to the visitation frequency of flies only. We constructed floral similarity measures based on the species’ floral traits and found that the floral similarity for all species’ traits combined and the continuous traits separately were positively related to individual visitation frequency. On the other hand, plant species with similar categorical floral traits did not have similar visitation frequencies. In conclusion, our results show that continuous traits, such as flower size and/or density, are more important for the variation in visitation frequency among plant species than thought earlier. Furthermore, differences in visitation frequency among pollinator groups give a poor support to the expectations derived from the classical pollination syndromes.