High-Resolution Three-Dimensional Structural Data Quantify the Impact of Photoinhibition on Long-Term Carbon Gain in Wheat Canopies in the Field

Contrasting leaf-scale photosynthetic low-light response and its temperature dependency are key to differences in crop-scale radiation use efficiency

Radiation use efficiency (RUE) is a key crop adaptation trait that quantifies the potential amount of aboveground biomass produced by the crop per unit of solar energy intercepted. But it is unclear why elite maize and grain sorghum hybrids differ in their RUE at the crop level. Here, we used a non-traditional top-down approach via canopy photosynthesis modelling to identify leaf-level photosynthetic traits that are key to differences in crop-level RUE. A novel photosynthetic response measurement was developed and coupled with use of a Bayesian model fitting procedure, incorporating a C4 leaf photosynthesis model, to infer cohesive sets of photosynthetic parameters by simultaneously fitting responses to CO2 , light, and temperature. Statistically significant differences between leaf photosynthetic parameters of elite maize and grain sorghum hybrids were found across a range of leaf temperatures, in particular for effects on the quantum yield of photosynthesis, but also for the maximum enzymatic activity of Rubisco and PEPc. Simulation of diurnal canopy photosynthesis predicted that the leaf-level photosynthetic low-light response and its temperature dependency are key drivers of the performance of crop-level RUE, generating testable hypotheses for further physiological analysis and bioengineering applications.

Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline

Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.

Photoacclimation and entrainment of photosynthesis by fluctuating light varies according to genotype in Arabidopsis thaliana

Acclimation of photosynthesis to light intensity (photoacclimation) takes days to achieve and so naturally fluctuating light presents a potential challenge where leaves may be exposed to light conditions that are beyond their window of acclimation. Experiments generally have focused on unchanging light with a relatively fixed combination of photosynthetic attributes to confer higher efficiency in those conditions. Here a controlled LED experiment and mathematical modelling was used to assess the acclimation potential of contrasting Arabidopsis thaliana genotypes following transfer to a controlled fluctuating light environment, designed to present frequencies and amplitudes more relevant to natural conditions. We hypothesize that acclimation of light harvesting, photosynthetic capacity and dark respiration are controlled independently. Two different ecotypes were selected, Wassilewskija-4 (Ws), Landsberg erecta (Ler) and a GPT2 knock out mutant on the Ws background (gpt2-), based on their differing abilities to undergo dynamic acclimation i.e. at the sub-cellular or chloroplastic scale. Results from gas exchange and chlorophyll content indicate that plants can independently regulate different components that could optimize photosynthesis in both high and low light; targeting light harvesting in low light and photosynthetic capacity in high light. Empirical modelling indicates that the pattern of 'entrainment' of photosynthetic capacity by past light history is genotype-specific. These data show flexibility of photoacclimation and variation useful for plant improvement.

A multiscale mathematical model describing the growth and development of bambara groundnut

A principal objective in agriculture is to maximise food production; this is particularly relevant with the added demands of an ever increasing population, coupled with the unpredictability that climate change brings. Further improvements in productivity can only be achieved with an increased understanding of plant and crop processes. In this respect, mathematical modelling of plants and crops plays an important role. In this paper we present a two-scale mathematical model of crop yield that accounts for plant growth and canopy interactions. A system of nonlinear ordinary differential equations (ODEs) is formulated to describe the growth of each individual plant, where equations are coupled via a term that describes plant competition via canopy-canopy interactions. A crop of greenhouse plants is then modelled via an agent based modelling approach in which the growth of each plant is described via our system of ODEs. The model is formulated for the African drought tolerant legume bambara groundnut (Vigna subterranea), which is currently being investigated as a food source in light of climate change and food insecurity challenges. Our model allows us to account for plant diversity and also investigate the effect of individual plant traits (e.g. plant canopy size and planting distance) on the yield of the overall crop. Informed with greenhouse data, model results show that plant positioning relative to other plants has a large impact on individual plant yield. Variation in physiological plant traits from genetic diversity and the environmental effects lead to experimentally observed variations in crop yield. These traits include plant height, plant carrying capacity, leaf accumulation rate and canopy spread. Of these traits plant height and ground cover growth rates are found to have the greatest impact on crop yield. We also consider a range of different planting arrangements (uniform grid, staggered grid, circular rings and random allocation) and find that the staggered grid leads to the greatest crop yield (6% more compared to uniform grid). Whilst formulated specifically for bambara groundnut, the generic formulation of our model means that with changes to certain parameter's, it may be extended to other crop species that form a canopy.

A 'wiring diagram' for source strength traits impacting wheat yield potential

Source traits are currently of great interest for the enhancement of yield potential; for example, much effort is being expended to find ways of modifying photosynthesis. However, photosynthesis is but one component of crop regulation, so sink activities and the coordination of diverse processes throughout the crop must be considered in an integrated, systems approach. A set of 'wiring diagrams' has been devised as a visual tool to integrate the interactions of component processes at different stages of wheat development. They enable the roles of chloroplast, leaf, and whole-canopy processes to be seen in the context of sink development and crop growth as a whole. In this review, we dissect source traits both anatomically (foliar and non-foliar) and temporally (pre- and post-anthesis), and consider the evidence for their regulation at local and whole-plant/crop levels. We consider how the formation of a canopy creates challenges (self-occlusion) and opportunities (dynamic photosynthesis) for components of photosynthesis. Lastly, we discuss the regulation of source activity by feedback regulation. The review is written in the framework of the wiring diagrams which, as integrated descriptors of traits underpinning grain yield, are designed to provide a potential workspace for breeders and other crop scientists that, along with high-throughput and precision phenotyping data, genetics, and bioinformatics, will help build future dynamic models of trait and gene interactions to achieve yield gains in wheat and other field crops.

Improving crop yield potential: Underlying biological processes and future prospects

The growing world population and global increases in the standard of living both result in an increasing demand for food, feed and other plant-derived products. In the coming years, plant-based research will be among the major drivers ensuring food security and the expansion of the bio-based economy. Crop productivity is determined by several factors, including the available physical and agricultural resources, crop management, and the resource use efficiency, quality and intrinsic yield potential of the chosen crop. This review focuses on intrinsic yield potential, since understanding its determinants and their biological basis will allow to maximize the plant's potential in food and energy production. Yield potential is determined by a variety of complex traits that integrate strictly regulated processes and their underlying gene regulatory networks. Due to this inherent complexity, numerous potential targets have been identified that could be exploited to increase crop yield. These encompass diverse metabolic and physical processes at the cellular, organ and canopy level. We present an overview of some of the distinct biological processes considered to be crucial for yield determination that could further be exploited to improve future crop productivity.

Has breeding altered the light environment, photosynthetic apparatus, and photosynthetic capacity of wheat leaves?

Whether photosynthesis has improved with increasing yield in major crops remains controversial. Research in this area has often neglected to account for differences in light intensity experienced by cultivars released in different years. Light intensity is expected to be positively associated with photosynthetic capacity and the resistance of the photosynthetic apparatus to high light but negatively associated with light-utilization efficiency under low light. Here, we analyzed the light environment, photosynthetic activity, and protein components of leaves of 26 winter wheat cultivars released during the past 60 years in China. Over time, light levels on flag leaves significantly decreased due to architectural changes, but photosynthetic rates under high or low light and the resistance of the photosynthetic apparatus to high light remained steady, contrary to expectations. We propose that the difference between the actual and expected trends is due to breeding. Specifically, breeding has optimized photosynthetic performance under high light rather than low light. Moreover, breeding selectivity altered the stoichiometry of several proteins related to dynamic photosynthesis, canopy light distribution, and photoprotection. These results indicate that breeding has significantly altered the photosynthetic mechanism in wheat and its response to the light environment. These changes likely have helped increase wheat yields.

Faster than expected Rubisco deactivation in shade reduces cowpea photosynthetic potential in variable light conditions

Cowpea is the major source of vegetable protein for rural populations in sub-Saharan Africa and average yields are not keeping pace with population growth. Each day, crop leaves experience many shade events and the speed of photosynthetic adjustment to this dynamic environment strongly affects daily carbon gain. Rubisco activity is particularly important because it depends on the speed and extent of deactivation in shade and recovers slowly on return to sun. Here, direct biochemical measurements showed a much faster rate of Rubisco deactivation in cowpea than prior estimates inferred from dynamics of leaf gas exchange in other species^1-3. Shade-induced deactivation was driven by decarbamylation, and half-times for both deactivation in shade and activation in saturating light were shorter than estimates from gas exchange (≤53% and 79%, respectively). Incorporating these half-times into a model of diurnal canopy photosynthesis predicted a 21% diurnal loss of productivity and suggests slowing Rubisco deactivation during shade is an unexploited opportunity for improving crop productivity.

The effect of canopy architecture on the patterning of "windflecks" within a wheat canopy

Under field conditions, plants are subject to wind-induced movement which creates fluctuations of light intensity and spectral quality reaching the leaves, defined here as windflecks. Within this study, irradiance within two contrasting wheat (Triticum aestivum) canopies during full sun conditions was measured using a spectroradiometer to determine the frequency, duration and magnitude of low- to high-light events plus the spectral composition during wind-induced movement. Similarly, a static canopy was modelled using three-dimensional reconstruction and ray tracing to determine fleck characteristics without the presence of wind. Corresponding architectural traits were measured manually and in silico including plant height, leaf area and angle plus biomechanical properties. Light intensity can differ up to 40% during a windfleck, with changes occurring on a sub-second scale compared to ~5 min in canopies not subject to wind. Features such as a shorter height, more erect leaf stature and having an open structure led to an increased frequency and reduced time interval of light flecks in the CMH79A canopy compared to Paragon. This finding illustrates the potential for architectural traits to be selected to improve the canopy light environment and provides the foundation to further explore the links between plant form and function in crop canopies.

Open source 3D phenotyping of chickpea plant architecture across plant development

BACKGROUND

Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea.

RESULTS

In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5-10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against "ground truth" measurements, producing R2 > 0.99 and a mean absolute percentage error < 10%. We demonstrate the ability to assess several important architectural traits, including canopy volume and projected area, and estimate relative growth rate in commercial chickpea cultivars and lines from local and international breeding collections. Detailed analysis of individual reconstructions also allowed us to investigate partitioning of plant surface area, and by proxy plant biomass.

CONCLUSIONS

Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.

Dynamics of photosynthetic induction and relaxation within the canopy of rice and two wild relatives

Wild rice species are a source of genetic material for improving cultivated rice (Oryza sativa) and a means to understand its evolutionary history. Renewed interest in non-steady-state photosynthesis in crops has taken place due its potential in improving sustainable productivity. Variation was characterized for photosynthetic induction and relaxation at two leaf canopy levels in three rice species. The wild rice accessions had 16%-40% higher rates of leaf CO2 uptake (A) during photosynthetic induction relative to the O. sativa accession. However, O. sativa had an overall higher photosynthetic capacity when compared to accessions of its wild progenitors. Additionally, O. sativa had a faster stomatal closing response, resulting in higher intrinsic water-use efficiency during high-to-low light transitions. Leaf position in the canopy had a significant effect on non-steady-state photosynthesis, but not steady-state photosynthesis. The results show potential to utilize wild material to refine plant models and improve non-steady-state photosynthesis in cultivated rice for increased productivity.

Perspectives on improving light distribution and light use efficiency in crop canopies

Plant stands in nature differ markedly from most seen in modern agriculture. In a dense mixed stand, plants must vie for resources, including light, for greater survival and fitness. Competitive advantages over surrounding plants improve fitness of the individual, thus maintaining the competitive traits in the gene pool. In contrast, monoculture crop production strives to increase output at the stand level and thus benefits from cooperation to increase yield of the community. In choosing plants with higher yields to propagate and grow for food, humans may have inadvertently selected the best competitors rather than the best cooperators. Here, we discuss how this selection for competitiveness has led to overinvestment in characteristics that increase light interception and, consequently, sub-optimal light use efficiency in crop fields that constrains yield improvement. Decades of crop canopy modeling research have provided potential strategies for improving light distribution in crop canopies, and we review the current progress of these strategies, including balancing light distribution through reducing pigment concentration. Based on recent research revealing red-shifted photosynthetic pigments in algae and photosynthetic bacteria, we also discuss potential strategies for optimizing light interception and use through introducing alternative pigment types in crops. These strategies for improving light distribution and expanding the wavelengths of light beyond those traditionally defined for photosynthesis in plant canopies may have large implications for improving crop yield and closing the yield gap.

The same anthocyanins served four different ways: Insights into anthocyanin structure-function relationships from the wintergreen orchid, Tipularia discolor

Over 500 unique anthocyanins have been described to date, which vary in color, antioxidant, light-attenuating, and antimicrobial properties. Identification of anthocyanin chemical structure may therefore serve as an important clue to their in situ function in plants. We characterized the histological and chemical structures of anthocyanins associated with diverse leaf color patterns in the terrestrial orchid, Tipularia discolor, as a step towards understanding their ultimate function. Tipularia discolor produces a single wintergreen leaf in autumn, which is drab brown in color during expansion. Upper (adaxial) surfaces of fully-expanded leaves may be green, purple-spotted, or solid purple, while lower (abaxial) surfaces are bright magenta. Our results showed that the same three cyanidin 3,7,3'-triglucosides, in similar concentrations and proportions, accounted for coloration in each of these cases, and that different colors result from differences in histological location of anthocyanins (i.e. abaxial/adaxial epidermis, mesophyll). Anthocyanins with 3,7,3' linkage positions are rare in plants, occurring only within the orchid subfamily Epidendroideae, to which Tipularia belongs. These results are important to the discussion of anthocyanin structure-function because they serve as a reminder that 1) plants may employ the same anthocyanins in different anatomical locations to achieve a broad range of colors (and potentially adaptive functions), and 2) anthocyanin chemical structure and anatomical location are influenced by phylogenetic inertia, as well as natural selection.

Photoprotective energy dissipation is greater in the lower, not the upper, regions of a rice canopy: a 3D analysis

High light intensities raise photosynthetic and plant growth rates but can cause damage to the photosynthetic machinery. The likelihood and severity of deleterious effects are minimised by a set of photoprotective mechanisms, one key process being the controlled dissipation of energy from chlorophyll within PSII known as non-photochemical quenching (NPQ). Although ubiquitous, the role of NPQ in plant productivity is important because it momentarily reduces the quantum efficiency of photosynthesis. Rice plants overexpressing and deficient in the gene encoding a central regulator of NPQ, the protein PsbS, were used to assess the effect of protective effectiveness of NPQ (pNPQ) at the canopy scale. Using a combination of three-dimensional reconstruction, modelling, chlorophyll fluorescence, and gas exchange, the influence of altered NPQ capacity on the distribution of pNPQ was explored. A higher phototolerance in the lower layers of a canopy was found, regardless of genotype, suggesting a mechanism for increased protection for leaves that experience relatively low light intensities interspersed with brief periods of high light. Relative to wild-type plants, psbS overexpressors have a reduced risk of photoinactivation and early growth advantage, demonstrating that manipulating photoprotective mechanisms can impact both subcellular mechanisms and whole-canopy function.

Variation in key leaf photosynthetic traits across wheat wild relatives is accession dependent not species dependent

The wild relatives of modern wheat represent an underutilized source of genetic and phenotypic diversity and are of interest in breeding owing to their wide adaptation to diverse environments. Leaf photosynthetic traits underpin the rate of production of biomass and yield and have not been systematically explored in the wheat relatives. This paper identifies and quantifies the phenotypic variation in photosynthetic, stomatal, and morphological traits in up to 88 wheat wild relative accessions across five genera. Both steady-state measurements and dynamic responses to step changes in light intensity are assessed. A 2.3-fold variation for flag leaf light and CO₂ -saturated rates of photosynthesis A_max was observed. Many accessions showing higher and more variable A_max , maximum rates of carboxylation, electron transport, and Rubisco activity when compared with modern genotypes. Variation in dynamic traits was also significant; with distinct genus-specific trends in rates of induction of nonphotochemical quenching and rate of stomatal opening. We conclude that utilization of wild relatives for improvement of photosynthesis is supported by the existence of a high degree of natural variation in key traits and should consider not only genus-level properties but variation between individual accessions.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible, and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.

Interpretation and Evaluation of Electrical Lighting in Plant Factories with Ray-Tracing Simulation and 3D Plant Modeling

In plant factories, light is fully controllable for crop production but involves a cost. For efficient lighting, light use efficiency (LUE) should be considered as part of light environment design. The objectives of this study were to evaluate and interpret the light interception, photosynthetic rate, and LUE of lettuces under electrical lights using ray-tracing simulation. The crop architecture model was constructed by 3D scanning, and ray-tracing simulation was used to interpret light interception and photosynthesis. For evaluation of simulation reliability, measured light intensities and photosynthetic rates in a growth chamber were compared with those obtained by simulation at different planting densities. Under several scenarios modeling various factors affecting light environments, changes in light interception and LUE were interpreted. The light intensities and photosynthetic rates obtained by simulation showed good agreement with the measured values, with R2 > 0.86. With decreasing planting density, the light interception of the central plant increased by approximately 18.7%, but that of neighboring plants decreased by approximately 5.5%. Under the various scenarios, shorter lighting distances induced more heterogenetic light distribution on plants and caused lower light interception. Under a homogenous light distribution, the light intensity was optimal at approximately 360 μmol m−2 s−1 with an LUE of 6.5 g MJ−1. The results of this study can provide conceptual insights into the design of light environments in plant factories.

Dynamic non-photochemical quenching in plants: from molecular mechanism to productivity

Photoprotection refers to a set of well defined plant processes that help to prevent the deleterious effects of high and excess light on plant cells, especially within the chloroplast. Molecular components of chloroplast photoprotection are closely aligned with those of photosynthesis and together they influence productivity. Proof of principle now exists that major photoprotective processes such as non-photochemical quenching (NPQ) directly determine whole canopy photosynthesis, biomass and yield via prevention of photoinhibition and a momentary downregulation of photosynthetic quantum yield. However, this phenomenon has neither been quantified nor well characterized across different environments. Here we address this problem by assessing the existing literature with a different approach to that taken previously, beginning with our understanding of the molecular mechanism of NPQ and its regulation within dynamic environments. We then move to the leaf and the plant level, building an understanding of the circumstances (when and where) NPQ limits photosynthesis and linking to our understanding of how this might take place on a molecular and metabolic level. We argue that such approaches are needed to fine tune the relevant features necessary for improving dynamic NPQ in important crop species.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

A canopy conundrum: can wind-induced movement help to increase crop productivity by relieving photosynthetic limitations?

Wind-induced movement is a ubiquitous occurrence for all plants grown in natural or agricultural settings, and in the context of high, damaging wind speeds it has been well studied. However, the impact of lower wind speeds (which do not cause any damage) on mode of movement, light transmission, and photosynthetic properties has, surprisingly, not been fully explored. This impact is likely to be influenced by biomechanical properties and architectural features of the plant and canopy. A limited number of eco-physiological studies have indicated that movement in wind has the potential to alter light distribution within canopies, improving canopy productivity by relieving photosynthetic limitations. Given the current interest in canopy photosynthesis, it is timely to consider such movement in terms of crop yield progress. This opinion article sets out the background to wind-induced crop movement and argues that plant biomechanical properties may have a role in the optimization of whole-canopy photosynthesis via established physiological processes. We discuss how this could be achieved using canopy models.

Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction

Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the construction of accurate 3D plant models is challenging, as plants are complex objects with an intricate leaf structure, often consisting of thin and highly reflective surfaces that vary in shape and size, forming dense, complex, crowded scenes. We address these issues within an image-based method by taking an active vision approach, one that investigates the scene to intelligently capture images, to image acquisition. Rather than use the same camera positions for all plants, our technique is to acquire the images needed to reconstruct the target plant, tuning camera placement to match the plant's individual structure. Our method also combines volumetric- and surface-based reconstruction methods and determines the necessary images based on the analysis of voxel clusters. We describe a fully automatic plant modeling/phenotyping cell (or module) comprising a six-axis robot and a high-precision turntable. By using a standard color camera, we overcome the difficulties associated with laser-based plant reconstruction methods. The 3D models produced are compared with those obtained from fixed cameras and evaluated by comparison with data obtained by x-ray microcomputed tomography across different plant structures. Our results show that our method is successful in improving the accuracy and quality of data obtained from a variety of plant types.

Three-dimensional plant architecture and sunlit-shaded patterns: a stochastic model of light dynamics in canopies

Background and Aims

Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterize light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resulting data are complex and high-dimensional. This necessitates development of more economical models for summarizing the data and for simulating realistic light patterns over the course of a day.

Methods

High-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 min) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit-shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from.

Key Results

For a wide range of contrasting 3-D canopies, the stochastic model is able to summarize, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data.

Conclusions

The model provides a way to summarize highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies.

Measuring the dynamic photosynthome

Background

Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The 'dynamic' changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield.

Scope

It is known that photosynthesis does not respond in a timely manner, and even a small temporal 'mismatch' between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered.

Conclusions

There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of 'phenomics' which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the 'photosynthome'. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.

Plant Phenomics, From Sensors to Knowledge

Major improvements in crop yield are needed to keep pace with population growth and climate change. While plant breeding efforts have greatly benefited from advances in genomics, profiling the crop phenome (i.e., the structure and function of plants) associated with allelic variants and environments remains a major technical bottleneck. Here, we review the conceptual and technical challenges facing plant phenomics. We first discuss how, given plants' high levels of morphological plasticity, crop phenomics presents distinct challenges compared with studies in animals. Next, we present strategies for multi-scale phenomics, and describe how major improvements in imaging, sensor technologies and data analysis are now making high-throughput root, shoot, whole-plant and canopy phenomic studies possible. We then suggest that research in this area is entering a new stage of development, in which phenomic pipelines can help researchers transform large numbers of images and sensor data into knowledge, necessitating novel methods of data handling and modelling. Collectively, these innovations are helping accelerate the selection of the next generation of crops more sustainable and resilient to climate change, and whose benefits promise to scale from physiology to breeding and to deliver real world impact for ongoing global food security efforts.

Enhanced thylakoid photoprotection can increase yield and canopy radiation use efficiency in rice

High sunlight can raise plant growth rates but can potentially cause cellular damage. The likelihood of deleterious effects is lowered by a sophisticated set of photoprotective mechanisms, one of the most important being the controlled dissipation of energy from chlorophyll within photosystem II (PSII) measured as non-photochemical quenching (NPQ). Although ubiquitous, the role of NPQ in plant productivity remains uncertain because it momentarily reduces the quantum efficiency of photosynthesis. Here we used plants overexpressing the gene encoding a central regulator of NPQ, the protein PsbS, within a major crop species (rice)  to assess the effect of photoprotection at the whole canopy scale. We accounted for canopy light interception, to our knowledge for the first time in this context. We show that in comparison to wild-type plants, psbS overexpressors increased canopy radiation use efficiency and grain yield in fluctuating light, demonstrating that photoprotective mechanisms should be altered to improve rice crop productivity.

Suboptimal Acclimation of Photosynthesis to Light in Wheat Canopies

Photosynthetic acclimation (photoacclimation) is the process whereby leaves alter their morphology and/or biochemistry to optimize photosynthetic efficiency and productivity according to long-term changes in the light environment. The three-dimensional architecture of plant canopies imposes complex light dynamics, but the drivers for photoacclimation in such fluctuating environments are poorly understood. A technique for high-resolution three-dimensional reconstruction was combined with ray tracing to simulate a daily time course of radiation profiles for architecturally contrasting field-grown wheat (Triticum aestivum) canopies. An empirical model of photoacclimation was adapted to predict the optimal distribution of photosynthesis according to the fluctuating light patterns throughout the canopies. While the photoacclimation model output showed good correlation with field-measured gas-exchange data at the top of the canopy, it predicted a lower optimal light-saturated rate of photosynthesis at the base. Leaf Rubisco and protein contents were consistent with the measured optimal light-saturated rate of photosynthesis. We conclude that, although the photosynthetic capacity of leaves is high enough to exploit brief periods of high light within the canopy (particularly toward the base), the frequency and duration of such sunflecks are too small to make acclimation a viable strategy in terms of carbon gain. This suboptimal acclimation renders a large portion of residual photosynthetic capacity unused and reduces photosynthetic nitrogen use efficiency at the canopy level, with further implications for photosynthetic productivity. It is argued that (1) this represents an untapped source of photosynthetic potential and (2) canopy nitrogen could be lowered with no detriment to carbon gain or grain protein content.

The Impacts of Fluctuating Light on Crop Performance

Rapidly changing light conditions can reduce carbon gain and productivity in field crops because photosynthetic responses to light fluctuations are not instantaneous. Plant responses to fluctuating light occur across levels of organizational complexity from entire canopies to the biochemistry of a single reaction and across orders of magnitude of time. Although light availability and variation at the top of the canopy are largely dependent on the solar angle and degree of cloudiness, lower crop canopies rely more heavily on light in the form of sunflecks, the quantity of which depends mostly on canopy structure but also may be affected by wind. The ability of leaf photosynthesis to respond rapidly to these variations in light intensity is restricted by the relatively slow opening/closing of stomata, activation/deactivation of C3 cycle enzymes, and up-regulation/down-regulation of photoprotective processes. The metabolic complexity of C4 photosynthesis creates the apparently contradictory possibilities that C4 photosynthesis may be both more and less resilient than C3 to dynamic light regimes, depending on the frequency at which these light fluctuations occur. We review the current understanding of the underlying mechanisms of these limitations to photosynthesis in fluctuating light that have shown promise in improving the response times of photosynthesis-related processes to changes in light intensity.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.

Exploring Relationships between Canopy Architecture, Light Distribution, and Photosynthesis in Contrasting Rice Genotypes Using 3D Canopy Reconstruction

The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies. However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops. The architecture of five genetically diverse rice varieties-four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64)-was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras. Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception. To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate (Pmax ) throughout canopy depth, hypothesizing that light is the sole determinant of productivity in these conditions. First, we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential. Second the predicted optimal Pmax responds in a manner that is consistent with fractional interception and leaf area index across this germplasm. However, measured Pmax , especially in lower layers, was consistently higher than the optimal Pmax indicating factors other than light determine photosynthesis profiles. Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency.

Don't ignore the green light: exploring diverse roles in plant processes

The pleasant green appearance of plants, caused by their reflectance of wavelengths in the 500-600 nm range, might give the impression that green light is of minor importance in biology. This view persists to an extent. However, there is strong evidence that these wavelengths are not only absorbed but that they also drive and regulate physiological responses and anatomical traits in plants. This review details the existing evidence of essential roles for green wavelengths in plant biology. Absorption of green light is used to stimulate photosynthesis deep within the leaf and canopy profile, contributing to carbon gain and likely crop yield. In addition, green light also contributes to the array of signalling information available to leaves, resulting in developmental adaptation and immediate physiological responses. Within shaded canopies this enables optimization of resource-use efficiency and acclimation of photosynthesis to available irradiance. In this review, we suggest that plants may use these wavelengths not just to optimize stomatal aperture but also to fine-tune whole-canopy efficiency. We conclude that all roles for green light make a significant contribution to plant productivity and resource-use efficiency. We also outline the case for using green wavelengths in applied settings such as crop cultivation in LED-based agriculture and horticulture.

Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems

Background and Aims

Intercropping systems contain two or more species simultaneously in close proximity. Due to contrasting features of the component crops, quantification of the light environment and photosynthetic productivity is extremely difficult. However it is an essential component of productivity. Here, a low-tech but high-resolution method is presented that can be applied to single- and multi-species cropping systems to facilitate characterization of the light environment. Different row layouts of an intercrop consisting of Bambara groundnut ( Vigna subterranea ) and proso millet ( Panicum miliaceum ) have been used as an example and the new opportunities presented by this approach have been analysed.

Methods

Three-dimensional plant reconstruction, based on stereo cameras, combined with ray tracing was implemented to explore the light environment within the Bambara groundnut-proso millet intercropping system and associated monocrops. Gas exchange data were used to predict the total carbon gain of each component crop.

Key Results

The shading influence of the tall proso millet on the shorter Bambara groundnut results in a reduction in total canopy light interception and carbon gain. However, the increased leaf area index (LAI) of proso millet, higher photosynthetic potential due to the C4 pathway and sub-optimal photosynthetic acclimation of Bambara groundnut to shade means that increasing the number of rows of millet will lead to greater light interception and carbon gain per unit ground area, despite Bambara groundnut intercepting more light per unit leaf area.

Conclusions

Three-dimensional reconstruction combined with ray tracing provides a novel, accurate method of exploring the light environment within an intercrop that does not require difficult measurements of light interception and data-intensive manual reconstruction, especially for such systems with inherently high spatial possibilities. It provides new opportunities for calculating potential productivity within multi-species cropping systems, enables the quantification of dynamic physiological differences between crops grown as monoculture and those within intercrops, and enables the prediction of new productive combinations of previously untested crops.

The High Light Response and Redox Control of Thylakoid FtsH Protease in Chlamydomonas reinhardtii

In Chlamydomonas reinhardtii, the major protease involved in the maintenance of photosynthetic machinery in thylakoid membranes, the FtsH protease, mostly forms large hetero-oligomers (∼1 MDa) comprising FtsH1 and FtsH2 subunits, whatever the light intensity for growth. Upon high light exposure, the FtsH subunits display a shorter half-life, which is counterbalanced by an increase in FTSH1/2 mRNA levels, resulting in the modest upregulation of FtsH1/2 proteins. Furthermore, we found that high light increases the protease activity through a hitherto unnoticed redox-controlled reduction of intermolecular disulfide bridges. We isolated a Chlamydomonas FTSH1 promoter-deficient mutant, ftsh1-3, resulting from the insertion of a TOC1 transposon, in which the high light-induced upregulation of FTSH1 gene expression is largely lost. In ftsh1-3, the abundance of FtsH1 and FtsH2 proteins are loosely coupled (decreased by 70% and 30%, respectively) with no formation of large and stable homo-oligomers. Using strains exhibiting different accumulation levels of the FtsH1 subunit after complementation of ftsh1-3, we demonstrate that high light tolerance is tightly correlated with the abundance of the FtsH protease. Thus, the response of Chlamydomonas to light stress involves higher levels of FtsH1/2 subunits associated into large complexes with increased proteolytic activity.

High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform

Light interception and radiation-use efficiency (RUE) are essential components of plant performance. Their genetic dissections require novel high-throughput phenotyping methods. We have developed a suite of methods to evaluate the spatial distribution of incident light, as experienced by hundreds of plants in a glasshouse, by simulating sunbeam trajectories through glasshouse structures every day of the year; the amount of light intercepted by maize (Zea mays) plants via a functional-structural model using three-dimensional (3D) reconstructions of each plant placed in a virtual scene reproducing the canopy in the glasshouse; and RUE, as the ratio of plant biomass to intercepted light. The spatial variation of direct and diffuse incident light in the glasshouse (up to 24%) was correctly predicted at the single-plant scale. Light interception largely varied between maize lines that differed in leaf angles (nearly stable between experiments) and area (highly variable between experiments). Estimated RUEs varied between maize lines, but were similar in two experiments with contrasting incident light. They closely correlated with measured gas exchanges. The methods proposed here identified reproducible traits that might be used in further field studies, thereby opening up the way for large-scale genetic analyses of the components of plant performance.

The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis

Physical perturbation of a plant canopy brought about by wind is a ubiquitous phenomenon and yet its biological importance has often been overlooked. This is partly due to the complexity of the issue at hand: wind-induced movement (or mechanical excitation) is a stochastic process which is difficult to measure and quantify; plant motion is dependent upon canopy architectural features which, until recently, were difficult to accurately represent and model in 3-dimensions; light patterning throughout a canopy is difficult to compute at high-resolutions, especially when confounded by other environmental variables. Recent studies have reinforced the expectation that canopy architecture is a strong determinant of productivity and yield; however, links between the architectural properties of the plant and its mechanical properties, particularly its response to wind, are relatively unknown. As a result, biologically relevant data relating canopy architecture, light- dynamics, and short-scale photosynthetic responses in the canopy setting are scarce. Here, we hypothesize that wind-induced movement will have large consequences for the photosynthetic productivity of our crops due to its influence on light patterning. To address this issue, in this study we combined high resolution 3D reconstructions of a plant canopy with a simple representation of canopy perturbation as a result of wind using solid body rotation in order to explore the potential effects on light patterning, interception, and photosynthetic productivity. We looked at two different scenarios: firstly a constant distortion where a rice canopy was subject to a permanent distortion throughout the whole day; and secondly, a dynamic distortion, where the canopy was distorted in incremental steps between two extremes at set time points in the day. We find that mechanical canopy excitation substantially alters light dynamics; light distribution and modeled canopy carbon gain. We then discuss methods required for accurate modeling of mechanical canopy excitation (here coined the 4-dimensional plant) and some associated biological and applied implications of such techniques. We hypothesize that biomechanical plant properties are a specific adaptation to achieve wind-induced photosynthetic enhancement and we outline how traits facilitating canopy excitation could be used as a route for improving crop yield.

Approaches to three-dimensional reconstruction of plant shoot topology and geometry

There are currently 805million people classified as chronically undernourished, and yet the World's population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention - particularly from computer vision researchers - and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements.

Photoprotection as a Trait for Rice Yield Improvement: Status and Prospects

Solar radiation is essential for photosynthesis and global crop productivity but it is also variable in space and time, frequently being limiting or in excess of plant requirements depending on season, environment and microclimate. Photoprotective mechanisms at the chloroplast level help to avoid oxidative stress and photoinhibition, which is a light-induced reduction in photosynthetic quantum efficiency often caused by damage to photosystem II. There is convincing evidence that photoinhibition has a large impact on biomass production in crops and this may be especially high in rice, which is typically exposed to high tropical light levels. Thus far there has been little attention to photoinhibition as a target for improvement of crop yield. However, we now have sufficient evidence to examine avenues for alleviation of this particular stress and the physiological and genetic basis for improvement in rice and other crops. Here we examine this evidence and identify new areas for attention. In particular we discuss how photoprotective mechanisms must be optimised at both the molecular and the canopy level in order to coordinate with efficient photosynthetic regulation and realise an increased biomass and yield in rice.