Plant Phenomics, From Sensors to Knowledge

An allometry perspective on crops

Understanding trait-trait coordination is essential for successful plant breeding and crop modeling. Notably, plant size drives variation in morphological, physiological, and performance-related traits, as described by allometric laws in ecology. Yet, as allometric relationships have been limitedly studied in crops, how they influence and possibly limit crop performance remains unknown. Here, we review how an allometry perspective on crops gains insights into the phenotypic evolution during crop domestication, the breeding of varieties adapted to novel conditions, and the prediction of crop yields. As allometry is an active field of research, modeling and manipulating crop allometric relationships can help to develop more resilient and productive agricultural systems to face future challenges.

Deciphering salt stress responses in Solanum pimpinellifolium through high-throughput phenotyping

Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.

Machine Learning-Based Tomato Fruit Shape Classification System

Fruit shape significantly impacts the quality and commercial value of tomatoes (Solanum lycopersicum L.). Precise grading is essential to elucidate the genetic basis of fruit shape in breeding programs, cultivar descriptions, and variety registration. Despite this, fruit shape classification is still primarily based on subjective visual inspection, leading to time-consuming and labor-intensive processes prone to human error. This study presents a novel approach incorporating machine learning techniques to establish a robust fruit shape classification system. We trained and evaluated seven supervised machine learning algorithms by leveraging a public dataset derived from the Tomato Analyzer tool and considering the current four classification systems as label variables. Subsequently, based on class-specific metrics, we derived a novel classification framework comprising seven discernible shape classes. The results demonstrate the superiority of the Support Vector Machine model in terms of its accuracy, surpassing human classifiers across all classification systems. The new classification system achieved the highest accuracy, averaging 88%, and maintained a similar performance when validated with an independent dataset. Positioned as a common standard, this system contributes to standardizing tomato fruit shape classification, enhancing accuracy, and promoting consensus among researchers. Its implementation will serve as a valuable tool for overcoming bias in visual classification, thereby fostering a deeper understanding of consumer preferences and facilitating genetic studies on fruit shape morphometry.

Artificial intelligence in plant breeding

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird's-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R 2 = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPAR f ), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.

The evolution of plant phenomics: global insights, trends, and collaborations (2000-2021)

Introduction

Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications.

Methods

The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET.

Results

Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges.

Discussion

The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China's rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community's adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties.

Regulation of a single inositol 1-phosphate synthase homeologue by HSFA6B contributes to fibre yield maintenance under drought conditions in upland cotton

Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fibre yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well-watered and water-limited conditions in the Arizona low desert were sequenced. Gene co-expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A-A and GhHSFA6B-D, associated with improved yield under water-limited conditions in an ABA-independent manner. DNA affinity purification sequencing (DAP-seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint yield-associated genes previously identified through genome-wide association studies (GWAS)-based approaches, GhABP-D and GhIPS1-A. Biochemical and phylogenetic approaches were used to determine that GhIPS1-A is positively regulated by GhHSFA6B-D, and that this regulatory mechanism is specific to Gossypium spp. containing the A (old world) genome. Finally, an SNP was identified within the GhHSFA6B-D binding site in GhIPS1-A that is positively associated with yield under water-limiting conditions. These data lay out a regulatory connection between abiotic stress and fibre yield in cotton that appears conserved in other systems such as Arabidopsis.

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

A system for the study of roots 3D kinematics in hydroponic culture: a study on the oscillatory features of root tip

BACKGROUND

The root of a plant is a fundamental organ for the multisensory perception of the environment. Investigating root growth dynamics as a mean of their interaction with the environment is of key importance for improving knowledge in plant behaviour, plant biology and agriculture. To date, it is difficult to study roots movements from a dynamic perspective given that available technologies for root imaging focus mostly on static characterizations, lacking temporal and three-dimensional (3D) spatial information. This paper describes a new system based on time-lapse for the 3D reconstruction and analysis of roots growing in hydroponics.

RESULTS

The system is based on infrared stereo-cameras acquiring time-lapse images of the roots for 3D reconstruction. The acquisition protocol guarantees the root growth in complete dark while the upper part of the plant grows in normal light conditions. The system extracts the 3D trajectory of the root tip and a set of descriptive features in both the temporal and frequency domains. The system has been used on Zea mays L. (B73) during the first week of growth and shows good inter-reliability between operators with an Intra Class Correlation Coefficient (ICC) > 0.9 for all features extracted. It also showed measurement accuracy with a median difference of < 1 mm between computed and manually measured root length.

CONCLUSIONS

The system and the protocol presented in this study enable accurate 3D analysis of primary root growth in hydroponics. It can serve as a valuable tool for analysing real-time root responses to environmental stimuli thus improving knowledge on the processes contributing to roots physiological and phenotypic plasticity.

Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling

The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model-the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.

Designing future peanut: the power of genomics-assisted breeding

KEY MESSAGE

Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

Rapid spatial assessment of leaf-absorbed irradiance

Image-based high-throughput phenotyping promises the rapid determination of functional traits in large plant populations. However, interpretation of some traits - such as those related to photosynthesis or transpiration rates - is only meaningful if the irradiance absorbed by the measured leaves is known, which can differ greatly between different parts of the same plant and within canopies. No feasible method currently exists to rapidly measure absorbed irradiance in three-dimensional plants and canopies. We developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature. Leaves were exposed to short light pulses (30 s) that were not long enough to trigger stomatal opening but strong enough to induce transient changes in leaf temperature that was proportional to the absorbed irradiance. The method was successfully validated against point measurements of absorbed irradiance in plant species with relatively simple architecture (sweet pepper, cucumber, tomato, and lettuce). Once calibrated, the model was used to produce absorbed irradiance maps from thermograms. Our method opens new avenues for the interpretation of plant responses derived from imaging techniques and can be adapted to existing high-throughput phenotyping platforms.

Plant responses to climate change, how global warming may impact on food security: a critical review

Global agricultural production must double by 2050 to meet the demands of an increasing world human population but this challenge is further exacerbated by climate change. Environmental stress, heat, and drought are key drivers in food security and strongly impacts on crop productivity. Moreover, global warming is threatening the survival of many species including those which we rely on for food production, forcing migration of cultivation areas with further impoverishing of the environment and of the genetic variability of crop species with fall out effects on food security. This review considers the relationship of climatic changes and their bearing on sustainability of natural and agricultural ecosystems, as well as the role of omics-technologies, genomics, proteomics, metabolomics, phenomics and ionomics. The use of resource saving technologies such as precision agriculture and new fertilization technologies are discussed with a focus on their use in breeding plants with higher tolerance and adaptability and as mitigation tools for global warming and climate changes. Nevertheless, plants are exposed to multiple stresses. This study lays the basis for the proposition of a novel research paradigm which is referred to a holistic approach and that went beyond the exclusive concept of crop yield, but that included sustainability, socio-economic impacts of production, commercialization, and agroecosystem management.

"Chamber #8" - a holistic approach of high-throughput non-destructive assessment of plant roots

Introduction

In the past years, it has been observed that the breeding of plants has become more challenging, as the visible difference in phenotypic data is much smaller than decades ago. With the ongoing climate change, it is necessary to breed crops that can cope with shifting climatic conditions. To select good breeding candidates for the future, phenotypic experiments can be conducted under climate-controlled conditions. Above-ground traits can be assessed with different optical sensors, but for the root growth, access to non-destructively measured traits is much more challenging. Even though MRI or CT imaging techniques have been established in the past years, they rely on an adequate infrastructure for the automatic handling of the pots as well as the controlled climate.

Methods

To address both challenges simultaneously, the non-destructive imaging of plant roots combined with a highly automated and standardized mid-throughput approach, we developed a workflow and an integrated scanning facility to study root growth. Our "chamber #8" contains a climate chamber, a material flow control, an irrigation system, an X-ray system, a database for automatic data collection, and post-processing. The goals of this approach are to reduce the human interaction with the various components of the facility to a minimum on one hand, and to automate and standardize the complete process from plant care via measurements to root trait calculation on the other. The user receives standardized phenotypic traits and properties that were collected objectively.

Results

The proposed holistic approach allows us to study root growth of plants in a field-like substrate non-destructively over a defined period and to calculate phenotypic traits of root architecture. For different crops, genotypic differences can be observed in response to climatic conditions which have already been applied to a wide variety of root structures, such as potatoes, cassava, or corn.

Discussion

It enables breeders and scientists non-destructive access to root traits. Additionally, due to the non-destructive nature of X-ray computed tomography, the analysis of time series for root growing experiments is possible and enables the observation of kinetic traits. Furthermore, using this automation scheme for simultaneously controlled plant breeding and non-destructive testing reduces the involvement of human resources.

P-MIRU, a Polarized Multispectral Imaging System, Reveals Reflection Information on the Biological Surface

Reflection light forms the core of our visual perception of the world. We can obtain vast information by examining reflection light from biological surfaces, including pigment composition and distribution, tissue structure and surface microstructure. However, because of the limitations in our visual system, the complete information in reflection light, which we term 'reflectome', cannot be fully exploited. For example, we may miss reflection light information outside our visible wavelengths. In addition, unlike insects, we have virtually no sensitivity to light polarization. We can detect non-chromatic information lurking in reflection light only with appropriate devices. Although previous studies have designed and developed systems for specialized uses supporting our visual systems, we still do not have a versatile, rapid, convenient and affordable system for analyzing broad aspects of reflection from biological surfaces. To overcome this situation, we developed P-MIRU, a novel multispectral and polarization imaging system for reflecting light from biological surfaces. The hardware and software of P-MIRU are open source and customizable and thus can be applied for virtually any research on biological surfaces. Furthermore, P-MIRU is a user-friendly system for biologists with no specialized programming or engineering knowledge. P-MIRU successfully visualized multispectral reflection in visible/non-visible wavelengths and simultaneously detected various surface phenotypes of spectral polarization. The P-MIRU system extends our visual ability and unveils information on biological surfaces.

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.

Comparative phenomics: a new approach to study heterochrony

Understanding the links between development and evolution is one of the major challenges of biology. 'Heterochronies', evolutionary alterations in the timings of development are posited as a key mechanism of evolutionary change, but their quantification requires gross simplification of organismal development. Consequently, how changes in event timings influence development more broadly is poorly understood. Here, we measure organismal development as spectra of energy in pixel values of video, creating high-dimensional landscapes integrating development of all visible form and function. This approach we termed 'Energy proxy traits' (EPTs) is applied alongside previously identified heterochronies in three freshwater pulmonate molluscs (Lymnaea stagnalis, Radix balthica and Physella acuta). EPTs were calculated from time-lapse video of embryonic development to construct a continuous functional time series. High-dimensional transitions in phenotype aligned with major sequence heterochronies between species. Furthermore, differences in event timings between conspecifics were associated with changes in high-dimensional phenotypic space. We reveal EPTs as a powerful approach to considering the evolutionary importance of alterations to developmental event timings. Reimagining the phenotype as energy spectra enabled continuous quantification of developmental changes in high-dimensional phenotypic space, rather than measurement of timings of discrete events. This approach has the possibility to transform how we study heterochrony and development more generally.

Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field

Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.

Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery

Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.

Stress-induced deeper rooting introgression enhances wheat yield under terminal drought

Water scarcity is the primary environmental constraint affecting wheat growth and production and is increasingly exacerbated due to climatic fluctuation, which jeopardizes future food security. Most breeding efforts to improve wheat yields under drought have focused on above-ground traits. Root traits are closely associated with various drought adaptability mechanisms, but the genetic variation underlying these traits remains untapped, even though it holds tremendous potential for improving crop resilience. Here, we examined this potential by re-introducing ancestral alleles from wild emmer wheat (Triticum turgidum ssp. dicoccoides) and studied their impact on root architecture diversity under terminal drought stress. We applied an active sensing electrical resistivity tomography approach to compare a wild emmer introgression line (IL20) and its drought-sensitive recurrent parent (Svevo) under field conditions. IL20 exhibited greater root elongation under drought, which resulted in higher root water uptake from deeper soil layers. This advantage initiated at the pseudo-stem stage and increased during the transition to the reproductive stage. The increased water uptake promoted higher gas exchange rates and enhanced grain yield under drought. Overall, we show that this presumably 'lost' drought-induced mechanism of deeper rooting profile can serve as a breeding target to improve wheat productiveness under changing climate.

Phenomics: conceptualization and importance for plant physiology

Phenomics is a relatively new discipline of biology that has been widely applied in several fields, mainly in crop sciences. We reviewed the concepts used in this discipline (particularly for plants) and found a lack of consensus on what defines a phenomic study. Furthermore, phenomics has been primarily developed around its technical aspects (operationalization), while the conceptual framework of the actual research lags behind. Each research group has given its own interpretation of this 'omic' and thus unwittingly created a 'conceptual controversy'. Addressing this issue is of particular importance, as the experimental designs and concepts of phenomics are so diverse that it is difficult to compare studies. In this opinion article, we evaluate the conceptual framework of phenomics.

Pitfalls and potential of high-throughput plant phenotyping platforms

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

Phenomics as an approach to Comparative Developmental Physiology

The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.

Harnessing the action mechanisms of microbial endophytes for enhancing plant performance and stress tolerance: current understanding and future perspectives

Microbial endophytes are microorganisms that reside within plant tissues without causing any harm to their hosts. These microorganisms have been found to confer a range of benefits to plants, including increased growth and stress tolerance. In this review, we summarize the recent advances in our understanding of the mechanisms by which microbial endophytes confer abiotic and biotic stress tolerance to their host plants. Specifically, we focus on the roles of endophytes in enhancing nutrient uptake, modulating plant hormones, producing secondary metabolites, and activating plant defence responses. We also discuss the challenges associated with developing microbial endophyte-based products for commercial use, including product refinement, toxicology analysis, and prototype formulation. Despite these challenges, there is growing interest in the potential applications of microbial endophytes in agriculture and environmental remediation. With further research and development, microbial endophyte-based products have the potential to play a significant role in sustainable agriculture and environmental management.

Engaging Precision Phenotyping to Scrutinize Vegetative Drought Tolerance and Recovery in Chickpea Plant Genetic Resources

Precise and high-throughput phenotyping (HTP) of vegetative drought tolerance in chickpea plant genetic resources (PGR) would enable improved screening for genotypes with low relative loss of biomass formation and reliable physiological performance. It could also provide a basis to further decipher the quantitative trait drought tolerance and recovery and gain a better understanding of the underlying mechanisms. In the context of climate change and novel nutritional trends, legumes and chickpea in particular are becoming increasingly important because of their high protein content and adaptation to low-input conditions. The PGR of legumes represent a valuable source of genetic diversity that can be used for breeding. However, the limited use of germplasm is partly due to a lack of available characterization data. The development of HTP systems offers a perspective for the analysis of dynamic plant traits such as abiotic stress tolerance and can support the identification of suitable genetic resources with a potential breeding value. Sixty chickpea accessions were evaluated on an HTP system under contrasting water regimes to precisely evaluate growth, physiological traits, and recovery under optimal conditions in comparison to drought stress at the vegetative stage. In addition to traits such as Estimated Biovolume (EB), Plant Height (PH), and several color-related traits over more than forty days, photosynthesis was examined by chlorophyll fluorescence measurements on relevant days prior to, during, and after drought stress. With high data quality, a wide phenotypic diversity for adaptation, tolerance, and recovery to drought was recorded in the chickpea PGR panel. In addition to a loss of EB between 72% and 82% after 21 days of drought, photosynthetic capacity decreased by 16-28%. Color-related traits can be used as indicators of different drought stress stages, as they show the progression of stress.

Eff-3DPSeg: 3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning

Reliable and automated 3-dimensional (3D) plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly supervised deep learning method was proposed for plant organ segmentation. The method contained (a) pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and (b) fine-tuning the pretrained model with about only 0.5% points being annotated to implement plant organ segmentation. After, 3 phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised setting. Our method achieved 95.1%, 96.6%, 95.8%, and 92.2% in the precision, recall, F1 score, and mIoU for stem-leaf segmentation for the soybean dataset and 53%, 62.8%, and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation for the Pheno4D dataset. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes. The trained networks are available at https://github.com/jieyi-one/EFF-3DPSEG.

Inside out: transforming images of lab-grown plants for machine learning applications in agriculture

Introduction

Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available.

Methods

In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images.

Results

Furthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images.

Discussion

The inclusion of training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data.

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches

Introduction

Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively.

Results

The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R² of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2).

Discussion

Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.

Silage maize as a potent candidate for sustainable animal husbandry development-perspectives and strategies for genetic enhancement

Maize is recognized as the queen of cereals, with an ability to adapt to diverse agroecologies (from 58^oN to 55^oS latitude) and the highest genetic yield potential among cereals. Under contemporary conditions of global climate change, C₄ maize crops offer resilience and sustainability to ensure food, nutritional security, and farmer livelihood. In the northwestern plains of India, maize is an important alternative to paddy for crop diversification in the wake of depleting water resources, reduced farm diversity, nutrient mining, and environmental pollution due to paddy straw burning. Owing to its quick growth, high biomass, good palatability, and absence of anti-nutritional components, maize is also one of the most nutritious non-legume green fodders. It is a high-energy, low-protein forage commonly used for dairy animals like cows and buffalos, often in combination with a complementary high-protein forage such as alfalfa. Maize is also preferred for silage over other fodders due to its softness, high starch content, and sufficient soluble sugars required for proper ensiling. With a rapid population increase in developing countries like China and India, there is an upsurge in meat consumption and, hence, the requirement for animal feed, which entails high usage of maize. The global maize silage market is projected to grow at a compound annual growth rate of 7.84% from 2021 to 2030. Factors such as increasing demand for sustainable and environment-friendly food sources coupled with rising health awareness are fueling this growth. With the dairy sector growing at about 4%-5% and the increasing shortage faced for fodder, demand for silage maize is expected to increase worldwide. The progress in improved mechanization for the provision of silage maize, reduced labor demand, lack of moisture-related marketing issues as associated with grain maize, early vacancy of farms for next crops, and easy and economical form of feed to sustain household dairy sector make maize silage a profitable venture. However, sustaining the profitability of this enterprise requires the development of hybrids specific for silage production. Little attention has yet been paid to breeding for a plant ideotype for silage with specific consideration of traits such as dry matter yield, nutrient yield, energy in organic matter, genetic architecture of cell wall components determining their digestibility, stalk standability, maturity span, and losses during ensiling. This review explores the available information on the underlying genetic mechanisms and gene/gene families impacting silage yield and quality. The trade-offs between yield and nutritive value in relation to crop duration are also discussed. Based on available genetic information on inheritance and molecular aspects, breeding strategies are proposed to develop maize ideotypes for silage for the development of sustainable animal husbandry.

Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images

The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m^-2) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R² = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m^-2, 0.4 m row spacing) towards 8 (12 plants.m^-2, 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m^-2, 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (R² = 0.98), and the accuracy was higher than only using one source point cloud data (R² = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance (R² = 0.94, root mean square error = 8.7). Complementary studies show that it is essential to simulate images with sufficient realism (background, leaf texture, and lighting conditions) before applying domain adaptation techniques. Furthermore, the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips. The method is claimed to be self-supervised since no manual labeling is required for model training. The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems. The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.

Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.

Model-based reconstruction of whole organ growth dynamics reveals invariant patterns in leaf morphogenesis

Plant organ morphogenesis spans several orders of magnitude in time and space. Because of limitations in live-imaging, analysing whole organ growth from initiation to mature stages typically rely on static data sampled from different timepoints and individuals. We introduce a new model-based strategy for dating organs and for reconstructing morphogenetic trajectories over unlimited time windows based on static data. Using this approach, we show that Arabidopsis thaliana leaves are initiated at regular 1-day intervals. Despite contrasted adult morphologies, leaves of different ranks exhibited shared growth dynamics, with linear gradations of growth parameters according to leaf rank. At the sub-organ scale, successive serrations from same or different leaves also followed shared growth dynamics, suggesting that global and local leaf growth patterns are decoupled. Analysing mutants leaves with altered morphology highlighted the decorrelation between adult shapes and morphogenetic trajectories, thus stressing the benefits of our approach in identifying determinants and critical timepoints during organ morphogenesis.

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.

Phenomics enables measurement of complex responses of developing animals to global environmental drivers

Phenomics offers technological advances for high-dimensional phenotyping, facilitating rapid, high-throughput assessment of physiological performance and has proven invaluable in global research challenges including drug discovery and food security. However, this rapidly growing discipline has remained largely inaccessible to the increasingly urgent challenge of assessing organismal functional sensitivity to global change drivers. Here, we investigate the response of an ecologically important marine invertebrate to multiple environmental drivers using Energy Proxy Traits (EPTs), a new approach for measuring complex phenotypes captured on video as a spectrum of energy levels across different temporal frequencies in fluctuating pixel values. We imaged three developmental stages of the common prawn Palaemon serratus at different salinities and temperatures, and measured EPTs and heart rate, a major proxy of physiological performance in ectotherms present across stages. Significant interactions were detected between temperature, developmental stage and salinity in frequency-specific energy levels. Despite cardiac activity being a significant contributor to the EPT spectra, treatment interactions were different from those observed on EPTs, highlighting additional phenotypic drivers of EPTs. Elevated temperature resulted in a shift of the EPT spectra towards higher frequency signals, indicating a reallocation of resources within the phenome. Using a non-linear dimensionality reduction, we interrogated the responses of EPT spectra in high-dimensional space. We discovered complex developmental-stage specific sensitivities, highlighting both the complexity of phenotypic responses, and the limits of using univariate approaches with pre-selected traits to assess responses to multiple global environmental drivers. EPTs are a high-dimensional, transferrable method of phenotyping, and are therefore highly relevant to addressing the current limitations of traditional methods of phenotyping applied to assessing biological sensitivity to drivers of global change. We predict that EPTs will become an important tool for indiscriminate phenotyping, transferrable between species, developmental stages and experimental designs.

RAV1 mediates cytokinin signaling for regulating primary root growth in Arabidopsis

Root growth dynamics is an outcome of complex hormonal crosstalk. The primary root meristem size, for example, is determined by antagonizing actions of cytokinin and auxin. Here we show that RAV1, a member of the AP2/ERF family of transcription factors, mediates cytokinin signaling in roots to regulate meristem size. The rav1 mutants have prominently longer primary roots, with a meristem that is significantly enlarged and contains higher cell numbers, compared with wild-type. The mutant phenotype could be restored on exogenous cytokinin application or by inhibiting auxin transport. At the transcript level, primary cytokinin-responsive genes like ARR1, ARR12 were significantly downregulated in the mutant root, indicating impaired cytokinin signaling. In concurrence, cytokinin induced regulation of SHY2, an Aux/IAA gene, and auxin efflux carrier PIN1 was hindered in rav1, leading to altered auxin transport and distribution. This effectively altered root meristem size in the mutant. Notably, CRF1, another member of the AP2/ERF family implicated in cytokinin signaling, is transcriptionally repressed by RAV1 to promote cytokinin response in roots. Further associating RAV1 with cytokinin signaling, our results demonstrate that cytokinin upregulates RAV1 expression through ARR1, during post-embryonic root development. Regulation of RAV1 expression is a part of secondary cytokinin response that eventually represses CRF1 to augment cytokinin signaling. To conclude, RAV1 functions in a branch pathway downstream to ARR1 that regulates CRF1 expression to enhance cytokinin action during primary root development in Arabidopsis.

QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

BACKGROUND

High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs.

RESULTS

We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants.

CONCLUSIONS

We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress

Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 °C day/night for 30 d, and then the temperature was increased for 7 d (38/31 °C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (G×E) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change.

Integration of high-throughput phenotyping with anatomical traits of leaves to help understanding lettuce acclimation to a changing environment

MAIN CONCLUSION

The combination of image-based phenotyping with in-depth anatomical analysis allows for a thorough investigation of plant physiological plasticity in acclimation, which is driven by environmental conditions and mediated by anatomical traits. Understanding the ability of plants to respond to fluctuations in environmental conditions is critical to addressing climate change and unlocking the agricultural potential of crops both indoor and in the field. Recent studies have revealed that the degree of eco-physiological acclimation depends on leaf anatomical traits, which show stress-induced alterations during organogenesis. Indeed, it is still a matter of debate whether plant anatomy is the bottleneck for optimal plant physiology or vice versa. Here, we cultivated 'Salanova' lettuces in a phenotyping chamber under two different vapor pressure deficits (VPDs; low, high) and watering levels (well-watered, low-watered); then, plants underwent short-term changes in VPD. We aimed to combine high-throughput phenotyping with leaf anatomical analysis to evaluate their capability in detecting the early stress signals in lettuces and to highlight the different degrees of plants' eco-physiological acclimation to the change in VPD, as influenced by anatomical traits. The results demonstrate that well-watered plants under low VPD developed a morpho-anatomical structure in terms of mesophyll organization, stomatal and vein density, which more efficiently guided the acclimation to sudden changes in environmental conditions and which was not detected by image-based phenotyping alone. Therefore, we emphasized the need to complement high-throughput phenotyping with anatomical trait analysis to unveil crop acclimation mechanisms and predict possible physiological behaviors after sudden environmental fluctuations due to climate changes.

The field phenotyping platform's next darling: Dicotyledons

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Translating CO[Formula: see text] variability in a plant growth system into plant dynamics

Plant growth occurs owing to the continuous interactions between environmental and genetic factors, and the analysis of plant growth provides crucial information on plant responses. Recent agronomic and analytical methodologies for plant growth require various channels for capturing broader and more dynamic plant traits. In this study, we provide a method of non-invasive growth analyses by translating CO[Formula: see text] variability around a plant. We hypothesized that the cumulative coefficient of variation (CCV) of plant-driven ambient CO[Formula: see text] variation in a plant growth system could yield a numerical indicator that is connected to the plant growth dynamics. Using the system outside-plant growth system-plant coupled dynamic model, we found that the CCV could translate dynamic plant growth under environmental and biophysical constraints. Furthermore, we experimentally demonstrated the application of CCV by using non-airtight growth chamber systems. Our findings may enrich plant growth information channels and assist growers or researchers to analyze plant growth comprehensively.

Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits

Advances in sugarcane breeding have contributed significantly to improvements in agronomic traits and crop yield. However, the growing global demand for sugar and biofuel in the context of climate change requires further improvements in cane and sugar yields. Attempts to achieve the desired rates of genetic gain in sugarcane by conventional breeding means are difficult as many agronomic traits are genetically complex and polygenic, with each gene exerting small effects. Unlike those of many other crops, the sugarcane genome is highly heterozygous due to its autopolyploid nature, which further hinders the development of a comprehensive genetic map. Despite these limitations, many superior agronomic traits/genes for higher cane yield, sugar production, and disease/pest resistance have been identified through the mapping of quantitative trait loci, genome-wide association studies, and transcriptome approaches. Improvements in traits controlled by one or two loci are relatively easy to achieve; however, this is not the case for traits governed by many genes. Many desirable phenotypic traits are controlled by quantitative trait nucleotides (QTNs) with small and variable effects. Assembling these desired QTNs by conventional breeding methods is time consuming and inefficient due to genetic drift. However, recent developments in genomics selection (GS) have allowed sugarcane researchers to select and accumulate desirable alleles imparting superior traits as GS is based on genomic estimated breeding values, which substantially increases the selection efficiency and genetic gain in sugarcane breeding programs. Next-generation sequencing techniques coupled with genome-editing technologies have provided new vistas in harnessing the sugarcane genome to look for desirable agronomic traits such as erect canopy, leaf angle, prolonged greening, high biomass, deep root system, and the non-flowering nature of the crop. Many desirable cane-yielding traits, such as single cane weight, numbers of tillers, numbers of millable canes, as well as cane quality traits, such as sucrose and sugar yield, have been explored using these recent biotechnological tools. This review will focus on the recent advances in sugarcane genomics related to genetic gain and the identification of favorable alleles for superior agronomic traits for further utilization in sugarcane breeding programs.