High-throughput Phenotyping Platform Research Articles

Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes

Recent advancements in artificial intelligence and big data analytics introduce new tools that can enhance the packing efficiency of sweetpotatoes (Ipomoea batatas) (SPs). In this study, we focused on the quantification of inventory as early in the packing process as possible to allow for effective storage planning, smarter inventory selection to fulfill orders, and ultimately reduce the need for refrigeration of excess packed SPs. We built and implemented two scanners to quantify phenotype distributions at different stages of the post-harvest pipeline. Testing and validation were conducted through a collaboration with an industry-partner's packing facility in North Carolina, gaining access to their packing methods, warehouse data, and resources. The first scanner imaged all SPs during the conveyance stage, immediately after they are washed but before they are sorted. The second scanner, positioned to view the top bins after harvest, scanned the top layer of bins on harvesting trucks as they entered the storage warehouse for receiving. We compared the output of our first scanner to the output of a commercial optical sorter under a controlled packing simulation, and then compared our two developed scanners against each other in an observational commercial packing operation. We evaluated millions of SPs, assessing length, width, length-to-width ratio (LW ratio), and weight. We computed a pairwise t-test for each phenotype across scanner pairs and evaluated the Cohen's d effect size to interpret our results. We observed no significant differences in the grade distributions across the scanners, except for the “Giant” weight class, which showed variation between the top bin and eliminator table scanners. In summary, both systems demonstrated promising outcomes, suggesting a potential enhancement in packing efficiency through the timely delivery of comprehensive inventory data.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars.

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

The Impact of Innovative Research Methods for Enhancing Agricultural Plants for Sustainable Development in The Future

In the pursuit of sustainable agricultural practices, the role of innovative research methods in enhancing agricultural plants is paramount. This abstract delineates the profound impact of such methodologies on sustainable development in the foreseeable future. Cutting-edge techniques such as genomic sequencing, molecular markers, and genome editing technologies revolutionize plant breeding by identifying and manipulating genes responsible for desirable traits, including disease resistance and drought tolerance. Omics technologies, encompassing transcriptomics, proteomics, and metabolomics, provide comprehensive insights into gene expression patterns and metabolic pathways, thereby facilitating targeted breeding strategies aimed at enhancing plant resilience and productivity. High-throughput phenotyping platforms enable rapid and accurate characterization of plant traits, empowering breeders to select superior varieties with enhanced performance under diverse environmental conditions. Concurrently, bioinformatics tools and data analytics play a pivotal role in deciphering complex genomic datasets, aiding in the identification of key genes and regulatory networks governing important agronomic traits. Biotechnology and synthetic biology approaches offer novel avenues for genetic manipulation, allowing for the design and engineering of plants with optimized traits, such as improved nutritional content and stress tolerance. Additionally, the integration of climate modeling with systems biology provides valuable insights into the interaction between genotype and environment, facilitating the development of climate-resilient crop varieties tailored to specific agroecosystems. Through the synergistic application of these innovative research methods, agricultural scientists and breeders can accelerate the pace of crop improvement while simultaneously promoting sustainability by fostering resilience, resource efficiency, and environmental stewardship. This transformative paradigm in agricultural research promises to address the complex challenges of food security, climate change, and environmental degradation, thereby laying the foundation for a more sustainable and resilient agricultural future.

LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union

BackgroundThe study of plant photosynthesis is essential for productivity and yield. Thanks to the development of high-throughput phenotyping (HTP) facilities, based on chlorophyll fluorescence imaging, photosynthetic traits can be measured in a reliable, reproducible and efficient manner. In most state-of-the-art HTP platforms, these traits are automatedly analyzed at individual plant level, but information at leaf level is often restricted by the use of manual annotation. Automated leaf tracking over time is therefore highly desired. Methods for tracking individual leaves are still uncommon, convoluted, or require large datasets. Hence, applications and libraries with different techniques are required. New phenotyping platforms are initiated now more frequently than ever; however, the application of advanced computer vision techniques, such as convolutional neural networks, is still growing at a slow pace. Here, we provide a method for leaf segmentation and tracking through the fine-tuning of Mask R-CNN and intersection over union as a solution for leaf tracking on top-down images of plants. We also provide datasets and code for training and testing on both detection and tracking of individual leaves, aiming to stimulate the community to expand the current methodologies on this topic.ResultsWe tested the results for detection and segmentation on 523 Arabidopsis thaliana leaves at three different stages of development from which we obtained a mean F-score of 0.956 on detection and 0.844 on segmentation overlap through the intersection over union (IoU). On the tracking side, we tested nine different plants with 191 leaves. A total of 161 leaves were tracked without issues, accounting to a total of 84.29% correct tracking, and a Higher Order Tracking Accuracy (HOTA) of 0.846. In our case study, leaf age and leaf order influenced photosynthetic capacity and photosynthetic response to light treatments. Leaf-dependent photosynthesis varies according to the genetic background.ConclusionThe method provided is robust for leaf tracking on top-down images. Although one of the strong components of the method is the low requirement in training data to achieve a good base result (based on fine-tuning), most of the tracking issues found could be solved by expanding the training dataset for the Mask R-CNN model.

A High-Throughput Approach for Photosynthesis Studies in a Brassicaceae Panel.

The study of natural variations in photosynthesis in the Brassicaceae family offers the possibility of identifying mechanisms to enhance photosynthetic efficiency in crop plants. Indeed, this family, and particularly its tribe Brassiceae, has been shown to harbor species that have a higher-than-expected photosynthetic efficiency, possibly as a result of a complex evolutionary history. Over the past two decades, methods have been developed to measure photosynthetic efficiency based on chlorophyll fluorescence. Chlorophyll fluorescence measurements are performed with special cameras, such as the FluorCams, which can be included in robotic systems to create high-throughput phenotyping platforms. While these platforms have so far demonstrated high efficiency in measuring small model species like Arabidopsis thaliana, they have the drawback of limited adaptability to accommodate different plant sizes. As a result, the range of species that can be analyzed is restricted. This chapter presents our approach to analyze the photosynthetic parameters: ϕPSII and Fv/Fm for a panel of Brassicaceae species, including a high-photosynthesis species, Hirschfeldia incana, and the adaptations to the phenotyping platform that are required to accommodate this varied group of plants.

A Review on High Throughput Phenotyping for Vegetable Crops

Abstract Conventional phenotyping approaches for vegetable crops such as Solanaceae, Bulb, and Root crops have contributed significantly to the development of numerous varieties. Despite this, traditional phenotyping procedures are insufficient because of the longer time required to produce a variety, poor genetic gain, environmental influences, and other externalities that impact phenotype-based selection. A novel recent approach of high throughput phenotyping (HTP) is regarded a potential tool for addressing the problems of traditional phenotyping. The advent of sensor, computer vision, automation, and sophisticated machine learning technologies sparked the creation of high-throughput phenotyping technology in the prior decade. HTP platforms are being used to conduct non-destructive evaluations of the whole plant system in a variety of crops. HTP provides precise measurements and suggests the collection of high-quality and accurate data, which is required for standardizing phenotyping for genetic dissection and genomic assisted breeding techniques such as genome-wide association studies (GWAS), linkage mapping, marker-assisted selection (MAS), and genomic selection (GS). The rest of this chapter examines the application of high-throughput phenotyping tools in genomic-assisted breeding for vegetable crops.

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.

Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images

BackgroundGrapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches.ResultsFirst, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day.ConclusionsWe successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.

Estimating leaf age of maize seedlings using UAV-based RGB and multispectral images

Leaf age is an essential parameter for describing the growth stage of crop. Thus, agronomists can develop timely cultivation strategies to promote maize growth according to leaf age. However, the traditional leaf age observation approach, which is inefficient, necessitates a large amount of people to investigate it in the field with destructive sampling. So, accurately quantifying leaf age under various maize types and production conditions remains a challenge. The purpose of this study was to develop a remote sensing monitoring approach for rapidly and non-destructively estimating the leaf age of maize seedlings. The UAV (unmanned aerial vehicle) high-throughput phenotyping platform was constructed to collect multi-source remote sensing images from maize emergence to jointing stage. Based on RGB and multispectral (MS) images, the image features of maize seedlings were extracted to construct the leaf age estimation models. The results showed that two regression models provided a reliable estimate performance of seedling leaf age, GBDT of which the best estimates are R2 of 0.88, Root Mean Square Error (RMSE) of 0.33, similarly, XGBoost being R2 of 0.89, RMSE of 0.32. The RGB-based model presented more accurate estimates (in terms of relative Root Mean Square Error of 9.26%) than the MS-based model (13.97%) and the RGB + MS-based model (12.26%). The results indicated that the maize seedling leaf age estimation method constructed in this study provides powerful technical support for agronomists to observe leaf age in the field.

OPIA: an open archive of plant images and related phenotypic traits.

High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566225 images with 2417186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.

OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features.

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object's pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and cosegmentation-based dense Conditional Random Fields (CRFs) to address segmentation accuracy in high-dimensional plant imagery with evolving plant objects. The efficacy of OSC-CO2 is demonstrated using plant growth sequences imaged with infrared, visible, and fluorescence cameras in multiple views using a remote sensing, high-throughput phenotyping platform, and is evaluated using Jaccard index and precision measures. We also introduce CosegPP+, a dataset that is structured and can provide quantitative information on the efficacy of our framework. Results show that OSC-CO2 out performed state-of-the art segmentation and cosegmentation methods by improving segementation accuracy by 3% to 45%.

High-throughput phenotyping and deep learning to analyze dynamic panicle growth and dissect the genetic architecture of yield formation

The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicles from the shoot in the image. Eventually, with these obtained i-traits, the yield could be well predicted, and the R2 was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.

Drought response of water-conserving and non-conserving spring barley cultivars.

Breeding barley cultivars adapted to drought requires in-depth knowledge on physiological drought responses. We used a high-throughput functional phenotyping platform to examine the response of four high-yielding European spring barley cultivars to a standardized drought treatment imposed around flowering. Cv. Chanell showed a non-conserving water-use behavior with high transpiration and maximum productivity under well-watered conditions but rapid transpiration decrease under drought. The poor recovery upon re-irrigation translated to large yield losses. Cv. Baronesse showed the most water-conserving behavior, with the lowest pre-drought transpiration and the most gradual transpiration reduction under drought. Its good recovery (resilience) prevented large yield losses. Cv. Formula was less conserving than cv. Baronesse and produced low yet stable yields. Cv. RGT's dynamic water use with high transpiration under ample water supply and moderate transpiration decrease under drought combined with high resilience secured the highest and most stable yields. Such a dynamic water-use behavior combined with higher drought resilience and favorable root traits could potentially create an ideotype for intermediate drought. Prospective studies will examine these results in field experiments and will use the newly gained understanding on water use in barley to improve process descriptions in crop simulation models to support crop model-aided ideotype design.

WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network

BackgroundDetection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications.ResultsIn our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61).ConclusionsExtensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications.

In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.

Integrating Non-photochemical Quenching (NPQ) Measurements for Identifying Flood-Tolerant Soybean Genotypes in the Era of Climate Change

Climate change has negatively affected agriculture worldwide, including soybean production. Studies have shown that rising temperatures and extreme weather events like droughts and floods significantly reduce soybean yields. Developing flood-tolerant soybean genotypes is crucial for ensuring food security. Conventional breeding programs are limited by laborious and imprecise visual rating methods for flooding tolerance identification. High-throughput platforms for plant phenotyping using imaging techniques offer potential solutions, but they lack information on underlying physiological mechanisms. Non-photochemical quenching (NPQ) is a molecular adaptation in photosynthesis that dissipates excess light energy, protecting plants from damage. This study aimed to integrate NPQ measurements into high-throughput phenotyping procedures to identify flooding-tolerant soybean genotypes. The study evaluated 160 soybean genotypes for flooding tolerance, identifying those with higher grain yield potential. Subsequently, ten genotypes were selected for monitoring NPQ responses under flooded conditions. Results showed that genotypes with higher grain yields also exhibited superior NPQ performance, suggesting a positive correlation between flooding tolerance and energy dissipation capacity. Among these genotypes, 58I60 RSF IPRO, 64HO130 I2X and BRS 525 displayed superior potential and could be further exploited in breeding efforts, considering their grain yield capacity, plant leaf area, and photoprotective capacity under flooding conditions. These findings suggest that integrating NPQ measurements into high-throughput phenotyping platforms can aid in identifying flood-tolerant soybean genotypes for breeding programs, leading to more resilient crops in the face of climate change. Further field studies are warranted to validate these hypotheses and improve crop models for future climate scenarios.

Using High-Throughput Phenotyping Analysis to Decipher the Phenotypic Components and Genetic Architecture of Maize Seedling Salt Tolerance.

Soil salinization is a worldwide problem that limits agricultural production. It is important to understand the salt stress tolerance ability of maize seedlings and explore the underlying related genetic resources. In this study, we used a high-throughput phenotyping platform with a 3D laser sensor (Planteye F500) to identify the digital biomass, plant height and normalized vegetation index under normal and saline conditions at multiple time points. The result revealed that a three-leaf period (T3) was identified as the key period for the phenotypic variation in maize seedlings under salt stress. Moreover, we mapped the salt-stress-related SNPs and identified candidate genes in the natural population via a genome-wide association study. A total of 44 candidate genes were annotated, including 26 candidate genes under normal conditions and 18 candidate genes under salt-stressed conditions. This study demonstrates the feasibility of using a high-throughput phenotyping platform to accurately, continuously quantify morphological traits of maize seedlings in different growing environments. And the phenotype and genetic information of this study provided a theoretical basis for the breeding of salt-resistant maize varieties and the study of salt-resistant genes.

A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids.

Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.

Integrated phenotyping of root and shoot growth dynamics in maize reveals specific interaction patterns in inbreds and hybrids and in response to drought.

In recent years, various automated methods for plant phenotyping addressing roots or shoots have been developed and corresponding platforms have been established to meet the diverse requirements of plant research and breeding. However, most platforms are only either able to phenotype shoots or roots of plants but not both simultaneously. This substantially limits the opportunities offered by a joint assessment of the growth and development dynamics of both organ systems, which are highly interdependent. In order to overcome these limitations, a root phenotyping installation was integrated into an existing automated non-invasive high-throughput shoot phenotyping platform. Thus, the amended platform is now capable of conducting high-throughput phenotyping at the whole-plant level, and it was used to assess the vegetative root and shoot growth dynamics of five maize inbred lines and four hybrids thereof, as well as the responses of five inbred lines to progressive drought stress. The results showed that hybrid vigour (heterosis) occurred simultaneously in roots and shoots and was detectable as early as 4 days after transplanting (4 DAT; i.e., 8 days after seed imbibition) for estimated plant height (EPH), total root length (TRL), and total root volume (TRV). On the other hand, growth dynamics responses to progressive drought were different in roots and shoots. While TRV was significantly reduced 10 days after the onset of the water deficit treatment, the estimated shoot biovolume was significantly reduced about 6 days later, and EPH showed a significant decrease even 2 days later (8 days later than TRV) compared with the control treatment. In contrast to TRV, TRL initially increased in the water deficit period and decreased much later (not earlier than 16 days after the start of the water deficit treatment) compared with the well-watered plants. This may indicate an initial response of the plants to water deficit by forming longer but thinner roots before growth was inhibited by the overall water deficit. The magnitude and the dynamics of the responses were genotype-dependent, as well as under the influence of the water consumption, which was related to plant size.

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.