Plant roots are responsible for essential functions like nutrient uptake, anchorage, and storage. Study of root uptake mechanisms for macro nutrients like nitrogen, phosphorus, potassium, and sulphur is vital to our understanding of their role in plant growth and development. Small signaling peptides (SSPs), are hormones which regulate diverse plant developmental processes including root growth. However, their involvement in regulation of nutrient uptake by roots is poorly understood. We recently developed a hydroponics- based plant growth system which combines ion chromatography with synthetic peptide application, to analyze the depletion rates of nutrients by Medicago truncatula roots. Application of the synthetic SSP MtCEP1 and AtCEP1 led to enhanced uptake of nitrates, sulphates, and phosphates. To further elucidate the molecular mechanism of nutrient uptake mediated by these peptides, we conducted an RNAseq of M. truncatula roots treated with the peptides. A differential gene expression analysis revealed thousands of peptide responsive genes. Several known nitrate transporters and a sulphate transporter AtSULTR3:5-like gene showed enhanced expression under both, MtCEP1 and AtCEP1 peptide application. Multiple, as of yet uncharacterized, CEP peptide responsive pathway regulatory genes such as kinases and transcription factors were also identified through this transcriptomic analysis. This study highlights the potential of phenomics enabled biology to uncover target genes for improving agriculturally important traits such as nutrient uptake.

Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses for farmers, and increased food costs at the supermarket. Root architecture plays a major role in a plant’s ability to survive and perform under drought conditions but phenotyping root growth to determine the genetic and environmental factors involved is extremely difficult due to roots being under the soil. RootBot is an automated high-throughput phenotyper that eliminates many of the difficulties and time constraints for performing multiple drought-stress studies. RootBot can simulate plant growth conditions during the first 72 hours of growth using transparent plates filled with soil (as opposed to synthetic media such as agar). RootBot has the capacity for up to 50 plates at a time, however, designing a system to organize these plates, image them at the appropriate times, and save and analyze the data for many plates simultaneously is challenging. To improve upon the pipeline, we incorporate strategies from existing phenotyping pipelines into the imaging and measurement processes. We will also investigate the genotypes using GWAS to identify sequence variants associated with drought tolerance or lack thereof. This pipeline will improve RootBot’s abilities in high-throughput phenotyping and the information gathered will be helpful towards future genetic engineering and breeding.

The rise of high-throughput phenotyping (HTP) has led to a dramatic increase in the ability to rapidly – and accurately – phenotype various organisms including plants. However, methods for efficiently managing, processing, analyzing, and sharing HTP data have not caught up to this new-found ability to collect big data which in turn introduces a whole host of new challenges. To address these, we have architected and implemented a multi-faceted infrastructure of webservices to further unify and automate the entire data collection process. We have integrated CyVerse and Slack into our IoHTP, two commonly used tools within plant science labs. CyVerse is a cloud-based data storage & management solution and Slack allows for bilateral instant-message communication with the HTP machines to keep researchers in touch with their autonomous experiments. Next, we are also developing our own website for administering jobs remotely to any number of (possibly geographically distributed) HTP machines. This innovative open-source approach has the potential to further advance high-throughput phenotyping worldwide by allowing interdisciplinary experts, namely in the plant and computer sciences, to collaborate more effectively and efficiently.

In an increasingly digitised and data-driven world, there is a pressing need for globally reproducible high-throughput morphological phenotyping, which provides quantitative and objective data and markers of seed quality to guide analysis and research. Current lab-based methods for morphological phenotyping of germinating seeds still largely rely on visual or 2D-imaging technologies with their respective limitations. Here we present the phenoTest, a novel high-throughput 3D-phenotyping technology that alleviates many of the drawbacks of conventional testing and research methods. Using Xray, 3D-volume image data of individual seedlings grown under highly-standardized conditions are captured. Through an AI-based algorithm, all plant organs can be automatically segmented and measured in 3D, currently outputting 50 seedling datasets per 2 minutes. Individual seedlings can be traced over time across the developmental process without disrupting the germination containers. The process can be run in fully-automated, 24h operation and is industrially validated for multiple years. The phenoTest is universally applicable with customized algorithms for all plants and crops, covering the entire range from fine grasses, vegetables to forestry seeds. The process enables a quantitative, objective and reproducible assessment of morphological seedling traits in 4D which can substitute visual gemination and vigor testing and can be harnessed to optimise processing and breeding. We will share data on the effects of a multitude of factors such as seed treatment, ageing, storage and packaging on the speed and quality of seedling development, and the homogeneity, degree of abnormalities, germination capacity and vigor of seed lots of different crop types.

Nitrogen inputs can be an important cost consideration for farmers in terms of economic profit as well as environmental impact. Elucidating genetic regions that are associated with plant phenotype response to nitrogen stress can help in facilitating breeding approaches that can mitigate these costs. A diverse population of 272 maize lines was planted at a field site in Champaign, IL in two consecutive years in reduced nitrogen conditions. 302 phenotypes were recorded including: seed ionomic content, root structural traits derived from 2-dimensional images as well as a 3-dimensional representation generated from x-ray computed tomography (XRT) scans, root traits extrapolated from mini-rhizotron systems, drone images across the growing season and end of season agronomic traits such as biomass and yield. BLUP models were fit to obtain estimates of single year genotypic values as well as across years values both of which took into account year specific spatial variation. Individual years and combined years BLUP values were used as response variables in genome wide association studies (GWAS) to identify loci significantly associated with each set of values. Significant associations were identified for all phenotype categories.

Anthropogenic factors such as climate change, harsh agricultural practices, and mining have contributed to increases in soil salinization and heavy metal contamination. Highly saline environments drastically lower yield for crop species and elevated levels of toxic metals like cadmium are carcinogenic in the environment. Some plants that have evolved in high-salinity habitats or in soils with heavy metals could be used to remediate contaminated soils. Halophytes are plants with various adaptations that allow them to survive and reproduce in saline conditions. General mechanisms for salt tolerance/uptake in halophytes are hypothesized to help deal with other stresses like heavy metals. Plants with these traits could be utilized to extract salt and heavy metals from affected soils in a process called phytoremediation. We plan to develop Sea Rocket (Cakile maritima) in the Mustard family as a model system to understand mechanisms of salt (NaCl) and cadmium uptake and tolerance, as it has been shown to accumulate both. As part of this study, we will hydroponically grow C. maritima in different stress treatments using salt and cadmium. As the plants uptake the pollutants, the conductivity of the solution will change. We will develop an automated pipeline to track these changes in real-time using conductivity sensors. In addition, we will sample root and leaf tissues at various time points to measure salt and cadmium uptake using ICP-OES elemental analysis. This data will provide insights into salt and cadmium uptake/tolerance and paves a path toward efficient and viable solutions improving phytoremediation approaches.

Generating data has become cheaper and easier, but alone is not sufficient to answer biological questions – data must be analyzed and interpreted. However, many algorithms can create or exacerbate biases (e.g., facial-recognition, ancestry, and disease risk). This necessitates incorporating diverse perspectives to confront both the moral and technical “big data challenges”. To move to a future where this is possible, it is necessary for researchers to develop skills in data management, processing, and analytics. Specifically, the field of plant phenotyping has moved from time consuming hand measurements to the use and development of high-throughput phenotyping. These systems require data-enabled/fluent users, yet academic programs in biology do not provide sufficient data science training. Here we present the Bioinformatics in Plant Science (BIPS) program at the University of Missouri (MU) as a model for training the next generation of data-enabled/fluent scientists. BIPS aims to mentor undergraduate students to build foundational skills in plant biology, research, and computational science. Our program pairs biology and computer science students to address biological questions through computational methods, with many focusing on plant phenotyping methods. The students learn to tackle problems using multidisciplinary approaches, alongside learning how to work in teams while building science communication skills (e.g., professional conferences, research forums, presenting to lawmakers). Through peer learning, BIPS students can understand and incorporate diverse perspectives from both the biological and computational side to address one of NSF’s 10 big ideas: harnessing the data revolution.

Tiny tomato pollen has an outsized role in reproduction, providing essential cellular and genetic material for fertilization and fruit generation. Unfortunately, high temperatures reduce pollination efficiency, harm fruit set and size, and ultimately diminish yield. This project attempts to answer basic questions about pollen growth and function during normal and heat-stressed conditions. Pollen from ~200 genetically diverse tomato and wild relative accessions will be observed as it grows at various temperatures. High-throughput microscopy will be paired with computer vision to phenotype the thousands of image sequences generated by this experiment. By combining pollen growth phenotypes with genome sequence data for all accessions, we plan to identify relevant genomic regions to target for functional description and crop improvement.

Phenotypic trait measurements have enabled breeders to link genomic information to phenotypic information and through this enhance crop performance by breeding for superior germplasm. Progress in this area has been hindered by the limited ability to capture agronomic traits of importance at a large field-based scale since traditional methods for measuring phenotypic traits in field are time and labor intensive and are limited in accuracy and consistency when implemented on large scale. Precision phenotyping efforts have enabled researchers at Corteva Agriscience to collect high quality datasets for important traits that programs had been unable to measure accurately or safely in the past. Specific applications of phenotyping technologies and how these have influenced data collection within Corteva breeding programs around the world will be presented. Technology is rapidly evolving, and by using this technology to develop novel precision phenotyping solutions, breeders are able to capture more high-quality data and gain unique insights.

Plant architecture is an important contributing factor for enhanced yield production and quality. The architecture traits are analyzed for crop health monitoring and genetic manipulation for generating high yielding varieties. Computer vision methods applied on 3D pointcloud allow more accurate extraction of architecture traits but consume more time and memory compared to 2D images. This study aims to design light weight 3D deep network for Cotton plant part segmentation and derive seven architectural traits of mainstem height, mainstem diameter, branch inclination angle, branch diameter, and number of branches, nodes, and cotton bolls. The pointcloud data is collected using FARO LiDAR scanner. The mainstem, branches and cotton bolls are manually annotated using Open3D. The preprocessing steps of denoising, normalization and down sampling are applied. 3D Deep network is designed to sample 1024, 512 and 256 points where neighborhood aggregation is performed at radius levels of 1cm, 5cm, and 30cm respectively. Features for remaining points are interpolated. The features from each radius level are concatenated and passed to multi-layer perceptron for pointwise classification. Results indicate that mean IoU and accuracy of 84% and 94% are achieved respectively. A 6.5 times speedup in inference time and 2.4 times reduction in memory consumption compared to Pointnet++ is gained. After applying postprocessing on part segments, an R square value of more than 0.8 and mean absolute percentage error of less than 11% are achieved on all derived architecture traits. The trait extraction results indicate potential utility of this process in plant physiology and breeding programs.

Plant roots exhibit distinct architectural organization and overall shape. Current concepts to quantify architectural variation assume a homogeneous phenotype for a given genotype. However, this assumption neglects the observable variation in root architecture for two reasons: (i) sampling strategies are designed to capture architectural variation only for the most common phenotype, and (ii) traits are often measured locally within a root system and ignore the architectural organization. Here, we introduce a new concept: the phenotypic spectrum of crop roots to quantify architectural variation as the number of architecture types for one genotype in a specific environment. We use the shape descriptor DS-curve to characterize the whole root system architecture. Using DS curves as a core, we developed a computing pipeline that combines Kmeans++ clustering, outlier filtering and the Fréchet distance as a similarity metric to classify types of root architectures. Subsequently, we applied this pipeline to analyze a field dataset including three common bean (Phaseolus vulgaris) genotypes DOR364 (n=797), L88_57 (n=1772), and SEQ7 (n=768) under non-limiting and water-stressed conditions in 2015 and 2016. We found DOR364 showed five different root architecture types across environments, while L88_57 and SEQ7 showed four. The total variation within classified root architecture types of DOR364, L88_57, and SEQ reduced by 58.59%, 50.19% and 53.01%, compared to the variation of the complete data sets. DOR364 had stable fractions of root architecture types across environments. In contrast, L88_57 and SEQ7 showed more variation in their fractions. There was no significant biomass difference among root architecture types for all studied genotypes within each environment. As such, we hypothesize that the phenotypic spectrum might buffer the impact of environmental stresses as an acclimatization strategy by changing the composition of root architecture types at the population level.

To assist plant scientists, geneticists, and growers to understand crop-environment interactions, plant phenotyping is a powerful tool for improving crop cultivars and developing decision support systems in farm management. Recent trends use LiDAR to capture three-dimensional (3D) information from plants to analyze traits vital to plant growth and development. However, current terrestrial-based 3D analysis methodologies are time and labor intensive and can be a bottleneck when large agricultural fields need to be analyzed. Robotic technologies can be used to accelerate the field-based measurements of relevant plant features and optimize the high-throughput phenotyping process. In this paper, we present a robotic system with a 3D LiDAR and a data processing pipeline for efficient, high-throughput field phenotyping of cotton crops. The robotic system consists of a Husky robotic platform equipped with a FARO Focus 3D laser scanner. The components of the system are integrated under the ROS framework to ensure interoperability and data integrity and availability at any given time. The data processing pipeline involves the data collection, registration, and analysis tasks for measuring crop traits at the plot level—canopy height, volume, and light interception—and estimating yield. This work demonstrates a crop phenotyping platform that leverages two off-the-shelf equipment for the quantitative assessment of cotton plant traits in the field. This methodology can be extended to other agricultural crops contributing to the advancement of plant phenomics.

Physiological dynamics at plant level are essential but also challenging for precision agriculture applications linked to plant phenotyping. In this study, we explore not only the spatial dynamics of corn in field conditions but also their temporal analysis via skeleton reconstruction of individual plants as a shape descriptor. For this purpose, an optimized approach for high-throughput was developed by point cloud data derived from UAS imagery. The curve-skeleton extraction is calculated based on a constrained Laplacian smoothing algorithm. The experimental setup was performed at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana, USA. On July 27th and August 3rd of 2021, two flights were performed over a trial with more than 200 maize plants using a custom designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor. RGB images were processed by a standard photogrammetric pipeline by Structure from Motion (SfM) to get a scaled 3D point cloud of the individual corn. Filtering techniques and labeling algorithms were joined together to reconstruct a robust and accurate skeleton of individual maize. Therefore, significant traits such as number, length, growth angle and elongation rate of leaves and stem can be easily extracted. Height variations computed from the skeleton at the two dates show a coefficient of correlation with on-field measurements better than 92%. Our experimental outcomes demonstrate the UAS-data’s ability to provide practical information to efficiently select phenotypes in plant breeding programs.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify their response to nutrient stress to determine the function of hooked hairs in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

Teff (Eragrostis tef) is an underutilized cereal grown primarily by small-scale farmers in Ethiopia, where it thrives under arid conditions unsuitable for other grain crops. Incomplete selection of classic domestication traits such as lodging, panicle architecture, and seed density contribute to the low yields observed in teff compared to leading cereals. To investigate the phenotypic basis of lodging tolerance in teff, we surveyed domestication related traits across a diversity panel of 265 teff wild relatives, landraces, and cultivars in Michigan. Panicle architecture and lodging score were collected in the field. To strengthen ground truth data and identify spectral signatures of plant height and subsequent lodging, LIDAR and hyperspectral images were collected with an unmanned aerial vehicle. A tiller imaging box was designed to maintain plant architecture from the field in a controlled lab environment. Morphological features including panicle height, panicle width, spikelet density, panicle angle, and tiller angle will be calculated using PlantCV and ImageJ. Feature evaluation via Pearson’s correlation and analysis of variance will be conducted for structural and morphological traits. This data will be used in a genome wide association study to identify phenotypes underlying lodging tolerance, and superior breeding material will be isolated for future studies.

As collections of data grow in size, it is increasingly important to have an efficient means of analyzing large data sets. Topological data analysis (TDA) applies concepts from the mathematical field of topology to not only efficiently examine large data sets, but to make inferences related to the overall “shape” of data. In this project, we use Mapper, a tool from TDA that summarizes data into a graph, to discover an underlying structure relating the shapes of more than 3,300 Passiflora leaves from 40 different species. We choose to study leaves of the Passiflora species in particular due to their extraordinary diversity of shape. As the Mapper graph has a structure, or “shape” of its own, we think of it as a “shape of shapes” that provides information on the interplay between the developmental processes determining leaf shape within a single plant and the evolutionary processes between species. In particular, we examine the interactions between leaf species and both heteroblasty and leaf area by constructing a Mapper graph for each measure. For each node in the resulting graphs, we then compute the average leaf shape to obtain a graph structure that reveals how morphometric differences between species relate to the developmental changes that must occur for those shapes to be realized.

Recent Innovations in Precision Agriculture (PA) are driven by Computer Vision and Data Processing systems to quantify plant parameters. Quantitative analysis of Plant Phenotyping in PA and monitoring morphological traits is a protracting process, precluding the objective and phenotyping pipeline. Greenhouses growing Genetically Modified (GM) crops need to be maintained at constant environmental and simulated conditions. Multiple parameters have to be controlled and regulated inside a greenhouse for effective growth of crops and yield maximisation. Not at all times are these factors derived and so, yield maximisation in greenhouse is an experimental approach to new varieties. For deduced environmental parameters and conditions for certain crops, few other biotic and abiotic factors can hinder or affect growth in certain ways that are not always factored in during calculating parameters conducive for plant growth. Such factors may not always be affecting parametral calculations, but transpose visual cues on plant growth environment such as spectral change in soil values, or minute changes like leaf reflectance or visible changes in plant stimuli to biotic factors. Plant growth is inclusive of multiple environmental variables, and yield maximisation approaches are experimental to finding the optimum derived value for these variables. Computer Vision provides a catalytic approach to predicting optimum parameters for yield maximization in phenomics. Computer Vision and Generative Adversarial Networks (GAN)’s offer a catalytic approach to the time-consuming process, providing a solution to the phenotyping bottleneck. This research proposes a concept of curating data of plant growth over time to predict conditional growth and responsive stimuli of the plant under different situations and how this can affect crop yield. The method proposed here is a non-invasive approach to the existing destructive biomass estimation methods and Frameworks. This methodology of the research focuses on utilizing image parameters modelled using a time series Progressively Growing Generative Adversarial Networks PGGAN to map plant growth patterns and progressive variance in biomass of plant in the Spatio-Temporal Domain. These Generative networks evaluate and predict based on merely raw pixel input excluding dependence on further constraints, feature vectors or parameters influencing data.

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical, and biotic properties of soil. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. Here I will present root phenotyping approaches for assessing ecosystem service in a prospective cash cover crop; pennycress (Thlaspi arvense L.). In development is a large format mesocosm system that will allow 3D root system architecture analysis of multiple plants. Using this system, we will be assessing how variation in pennycress root system architecture can affect ecosystem service and abiotic stress tolerance with the plant to scale from single plant to canopy level traits.