The Columbia root-knot nematode (Meloidogyne chitwoodi) is a destructive soil borne pest that can cause serious economic damage to potato tubers within infected, unfumigated fields. There are very few known sources of genetic resistance to root-knot nematodes and no released potato cultivars exhibit this trait. Literature indicates that nematode resistance introgressed from Solanum bulbocastanum is dominantly inherited across many genetic backgrounds. We generated a 32 family half-diallel progeny test population utilizing 3 root-knot nematode resistant clones (female) and 11 clones (male) with russet skin type (1,600 clones, between 25 – 60 clones per family). In 2023, 1,200 progeny were evaluated relative to 6 control varieties planted at high replication at the Washington State University Experiment Station in Othello, WA. A scale, RGB-D imaging conveyor, and index scoring system was used to record: total yield, the number of tubers per plant, tuber size distribution, aspect ratio, skin color, starch content, and defect severity from all samples within this population. The distribution of phenotypic values observed from the progeny suggest that choice of parent is a highly significant factor that influences almost all traits evaluated. The phenotyping strategy utilized by our team is inexpensive, expandable, and flexible enough to be adopted by many different types of vegetable breeding programs. Data from this experiment is being used to develop potato tuber defect classification models and assess the utility of adopting genomic selection within our potato breeding program.

Light levels change throughout the day, are affected by climate and weather, and are filtered by the local environment. Switching between low and high levels of light over varying periods of time experienced by an organism in its environment shapes the tempo and mode of its light detection system. Plants must respond to dynamic environmental conditions and thus switch between efficient photosynthesis and photoprotection. Receptors on the plasma membrane perceive extracellular signals, such as photosynthetically-fixed sugars, are coupled to cytoplasmic G proteins to transduce information to cytoplasmic proteins and to amplify that signal to bring about changes like photosynthetic efficiency in both short (e.g. enzymatic reactions) and long (e.g. plant development) time scales. While G proteins have been shown to be important in regulating various aspects of stomata and photosynthesis, their role has yet to be fully understood. A regulator of G signaling (RGS) has been shown to sense sugars fixed in photosynthesis. Thus, we hypothesize that RGS mediates responses to dynamic light. The sequenced genomes within the grass family are the only genomes throughout Plantae known to lack RGS. By contrast, Setaria retains the RGS gene. Thus, the RGS gene from Setaria was expressed in rice to better understand the function of RGS. In this study, multiple transgenic events were grown to investigate their phenotypic response. We identified lines with altered stomatal patterning and rates of stomatal closure in response to changing light levels that will be used in future experiments.

Quantifying 3D phenotypic traits for plant shoots and roots is essential to monitor and evaluate plant growth and development. Multi-view stereo (MVS) is a low-cost and widely used photogrammetry method to build 3D point clouds in many agricultural applications. However, it is challenging to adopt MVS directly to obtain complete 3D structures of fine roots for plants such as soybeans. To address this problem, we propose a data processing pipeline incorporating super-resolution (SR) and 3D Gaussian Splatting (GS) to enhance the resolution of 3D root reconstruction, aiming to recover a highly detailed 3D root structure. To this end, first, multi-view images of a soybean root are collected using an RGB camera; second, SR is used to optimize the resolution of the images; third, the processed images are fed to the algorithm structure from motion to obtain a point cloud; and then, 3D GS is applied to enhance the implicit 3D surface reconstruction; finally, perceptual similarity and peak signal-to-noise ratio (PSNR) are used to evaluate the output quality. The method is expected to obtain a high-fidelity 3D reconstruction of plant roots for soybeans and other crops, assisting in the extraction of comprehensive phenotypic traits to accelerate the selection of new varieties for plant breeding.

Advances in quantitative genetics and high-throughput pipelines have allowed for rapid identification of genomic markers associated with changes in phenotype. However, linking those markers to causal genes is still difficult, as many genes may be linked to one marker. We aimed to improve candidate gene selection by creating a new method that identifies conserved genes underlying GWAS loci in multiple species. So far, we have tested this method in two different experiments: 1) using GWAS from Arabidopsis, soybean, rice, maize, and sorghum measuring 19 elemental uptake (ionomic) traits and 2) GWAS from Arabidopsis, soybean, rice, and maize measuring seed weight traits. We identified 14,336 candidates in the ionomics GWAS comparison. The most likely candidates belonged to ortholog groups linked to GWAS loci in all five species for their given trait according to calculations using random permutations of the data. For the seed weight GWAS comparison, we identified 192 candidates, and again, the most likely candidates belonged to ortholog groups linked to GWAS loci in all species in the comparison. Focusing on these most likely candidate genes from Arabidopsis, we obtained T-DNA lines with mutant alleles for each candidate gene and performed a high-throughput phenotyping screen utilizing ICP-MS for ionomics and the image analysis software PlantCV for seed weight. Preliminary results show 59 ionomic candidates and 9 seed weight candidates have one line with confirmed phenotypes. We plan to further verify these preliminary confirmations by obtaining and screening additional T-DNA lines with different alleles for each candidate gene.

Microbiome and metabolic activity of the Soybean nodule changed by the Rhizobacteria and Fusarium complexM. Saleem1, Md Imam ul Khabir1 , Jannat Jhumur1 ,Z. H. Pervaiz21Department of Biological Sciences, Alabama State University2Department of Biological Sciences, Auburn UniversityKeyword : Microbiome, DNA Extraction,16S RNA,Sequencing, Microbial community, Microbial Diversity,OTU,Nodule Microbiome,BNF.Soybean plants develop symbiotic associations with rhizobia to form nodules in which the biological nitrogen fixation (BNF) occurs, which is an important nitrogen source for plant growth. Though soybean root-microbiome interactions are studies, we know little about nodule microbiome and metabolome, and how it is influenced by beneficial and pathogenic microbes. Here,we investigated the effect of rhizobacterial (growth promoting) and Fusarium spp .,(causing root rot disease) consortia on nodule microbiome and metabolome via seed inoculation in a field experiment.The soybean seeds were inoculated withbeneficial and pathogenic consortia while thecontrol represented the un-inoculated seeds. These seeds were sown in an experimental field soil managed by the University of Nebraska,Lincoln (UNL).The nodules were collected from soybean plants and DNA was extracted.The frozen nodules were crushed in the super-deionized water and metabolome analysis was done at the UNL.The 16S rRNA gene amplicon sequencing was done at the University of Minnisota.Though Proteobacteria dominanted the nodule microbiome, the abundance of other phyla was also significant across all treatments.Both consortia suppressed dominant bacterial families (including population of Bradyrhizobiumsp .)while rhizobacterial consortia incrased the diversity of nodule microbiome.The soybean nodules exhibited a rich community of bacterial phyla while microbiome was dominated by Proteobacteria in all treatments. Pathogen and beneficial consortia suppressed Proteobacteria though later increased OTU diversity and decreased the abundance ofBradyrhizobiumsp . Pathogen increased amino acids and organic compounds contents while rhizobacteria increased the contents of sugar acids, sugar alcohol, and organic acids.So,pathogen and benecifial consortia altered nodule microbiome and metabolome though their effects on BNF remain understudied.

Evaluation and improvement of Type III resistance (lower mycotoxin accumulation) is an integral part in developing wheat varieties with resistance to Fusarium Head Blight. Therefore, application of novel tools is necessary to increase selection accuracy and intensity. Here, we explored the application of phenomic prediction using hyperspectral imaging in predicting Deoxynivalenol (DON) content in soft winter wheat kernels. In all Bayesian prediction models used, phenomic prediction recorded higher accuracy (0.63-0.67) than genomic prediction (0.55-0.60). Following this, we proceeded to use the trained prediction models: Bayes C, Bayesian Ridge Regression, and Bayesian LASSO in a testing set of F4:5 breeding lines. Selection was carried out using Unsupervised K-Means Clustering. A large proportion of F4:5 breeding lines predicted to have low DON content were also observed to have low GC/MS-derived DON content. The results of this study revealed the potential application of hyperspectral imaging in predicting Deoxynivalenol accumulation in soft winter wheat kernels with increased selection intensity.

Dry bean (Phaseolus vulgaris L.) is the third largest pulse crop grown in Canada. Due to climate change and extreme weather, dry bean varieties are subjected to abiotic and biotic stresses, which affect yield stability and seed quality. Development of resilient cultivars is the most effective strategy to ensure productivity and environmental sustainability of dry bean crop. In this project, key phenotypic traits will be extracted for genetic improvement and development of elite cultivars with early maturity and high yield. Traditional phenotyping approaches are rigorous, time-consuming, and subject to human errors. Unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) has been changing the way of doing large-scale phenotyping in plant breeding. The use of aerial imaging systems offers a potential solution to provide an intensive tool for complex traits assessment to evaluate a large number of dry bean genotypes. By this, HTP technique will be optimized to improve selection efficiency of agronomic, physiological and disease resistance traits. In this study, two dry bean field trials, Advanced Yield Trial (AYT) consisting of F7 generation [yellow bean (5 entries), Pinto bean (20 entries)], and Performance Yield Trial (PeYT) of F8-F10 generation (49 entries) were grown in a randomized-block design at the Fairfield Research Farm at AAFC Lethbridge, AB. Both field trials were imaged at the specific developmental stages (vegetative, flowering, maturity) using UAV mounted RGB and multispectral sensors. The acquired imagery have been processed to accurately overlay images from different dates (time-series data comparison). We analyzed three-time point RGB and multispectral images to identify valuable traits such as canopy height, crop lodging, physiological maturity and accumulation of crop biomass over time. With the preliminary results, we found the utilization of UAV-based HTP has significant advantage in non-destructive measurements of canopy-level functional traits. Assessment of these traits at same climatic region can be used to identify crop characteristics that are important for screening of high-quality dry bean experimental lines and cultivars in field conditions. In the long term, it will provide a consistent and reliable information system to rapidly screen thousands of breeding populations individually that need to be genotyped for morphological and physiological functional traits.

Digital imaging technology has gained significant interest in recent decades, particularly in the field of high-throughput phenotyping (HTP) for plant breeding. Breeding programs generates thousands of new crop lines that require evaluation under multiple environments. Considerable efforts have been made in utilizing genome wide association studies (GWAS) and genomic selection (GS) to identify genetic markers and improve desirable crop characteristics. Selecting key phenotypes is an essential component of plant breeding, and traditional methods require considerable resources and are subjective. Therefore, breeders and geneticists are in an urge of a robust technology to identify desirable crop traits. HTP using advanced sensors is a promising approach to evaluate improved crop genotypes for traits of agronomic importance. In this project, six Research and development Centers (RDCs) of Agriculture and Agri-food Canada have been utilizing University of Saskatchewan built Field Phenotyping System ("UFPS Cart") to phenotype a heritage bread wheat panel. The UFPS cart is a proximal sensing mobile platform equipped with multiple payloads (RTK GPS, RGB, NIR, and LiDAR sensor). For diverse climatic data collection, the panel consisting of 30 Canadian western spring wheat varieties were grown under six environments. This study aims to develop large-scale data management and image analysis pipelines to quantify different crop growth characteristics representing agronomic and physiological traits. It support data-driven decision making under genotype × environment effect. The multi-location imagery and ground observation data from six environments are currently being processed using the internal General Public Science Cluster (GPSC) for deep learning training to develop prediction models and extract phenotypic traits of interest (canopy height, crop lodging, heading, maturity, grain yield and protein content). The developed tools and associated models will aid to accelerate advances in cereal breeding programs.

ORCiD: [https://orcid.org/0000-0003-0655-2343] Keywords: Root imaging, root-system architecture (RSA), soybean, 2D-phenotyping & 3D-phenotyping Roots are a major part of plant systems and are essential to obtaining water and nutrients. Despite their importance, roots have not been extensively examined as compared to their aboveground counterparts, due primarily to the difficulties of access and lack of standard methods to quantify root morphology. While there have been several experiments performed under controlled environments, comparatively fewer studies have examined root architectures under field conditions. Here, we apply two imaging techniques to characterize variability in Root System Architecture (RSA) in diverse soybean genotypes under field settings with two contrasting soil conditions. Thus, our objectives are to (1) quantify root system architecture using 2D image techniques (e.g., Winrhizo and Image J) and (2) evaluate a contrasting subset of these samples (n = 30) using a novel 3D phenotyping approach. The research seeks to meet the need for enhanced methods in root system architecture analysis across diverse field conditions potentially leading to more resilient, high-yielding soybean varieties.

With increasing demands for sustainable food production, expediting innovation within the development of agricultural products is paramount for Bayer and similar companies. We used a novel gene-editing protocol that generates numerous events to increase our gene-editing capacity. TREDMIL was used create 800 distinct edits in soybeans at three Dt1 gRNA targets in over 1500 events sites distributed across 100 soy lines. With the ability to produce great numbers of edit events, our phenotypic testing had to evolve to keep pace. Using a hypothesis-based approach, we have refined phenotypic testing to measure relevant plant traits that impact yield. Our in-field phenotyping of the target set of plant traits feeds a machine learning model that adjusts small plot yield. Testing in both corn and soy has demonstrated small plots are predictive of large-scale yield testing (84% agreement) and that modeling with additional traits improved the predictive capacity to 93%.

Non-destructive, real-time monitoring of root development can be helpful to farmers in improving crop resilience while minimizing resource use (Mervin et al., 2022). However, it is still an unexplored frontier in understanding root responses efficiently. In this study, we employed three in-soil fiber Bragg grating (FBG) based fiber sensors to generate root phenotyping data and developed an automated method using the deep learning architecture ResNet to monitor underground root development. In our preliminary study, we conducted a simulation experiment using two metal rods with diameters of 1mm and 5mm to mimic plant's roots. These rods were inserted to a depth of 15 cm in two different scenarios, 6 and 11 minutes, with the three in-soil FBG sensors continuously collecting data-two FBGs placed on the sides, and one placed at the bottom. The sensor data was preprocessed, resulting in 3228 samples for root diameter and 477 for root depth prediction models. We used an 80/20 split for training and testing the ResNet models to predict the artificial root diameter and ten different depth levels. The achieved accuracy was 0.95 for depth and 0.91 for diameter prediction. Overall, our study demonstrates the potential of ResNet architectures to accurately predict root depth and diameter with fiber optics-based sensors. Therefore, non-destructive root phenotyping in agricultural applications might be possible. Future work will involve evaluating these models in field experiments to assess their real-world performance.

Agriculture utilizes large quantities of freshwater resources to maintain crop production to a level that meets global demand. As the world population expands at a rapid rate, it is critical that we find more efficient ways to manage freshwater resources. The Plant DiTech Phenotyping Platform (Plant-DiTech LTD, Yavne, Israel) is a dynamic plant screening system that allows researchers to create controlled environments and rapidly collect a large quantity of physiological traits in real time. This platform opens the door to hundreds of potential experiments to explore growth trait responses to future environmental conditions and irrigation practices. Here, we test its utility on cultivated rice and upland cotton in a small pilot experiment. Cultivated rice (Oryza sativa ) and upland cotton (Gossypium hirsutim ) are both C3 crop species that are grown throughout the U.S. and many other countries and are critical for food and fiber production, respectively. Cotton is typically produced in the warm and dry regions of southern U.S. (USDA-ERS) where it heavily relies on irrigation practices to remain productive, while rice requires flooded conditions to maintain high productivity. Using these two test species for a preliminary experiment, a series of growth trait measurements in conjunction with system-recorded data by the Plant DiTech phenotyping platform are analyzed to test the linkages between organ- and canopy-level traits.

In recent years, the use of field-based high-throughput phenotyping (FHTP) has surged across diverse disciplines. Particularly, it has gained significant traction in agricultural research, enabling scientists to efficiently gather extensive data for a deeper understanding of plant biology in the context of plant growth dynamics. This abstract aims to demonstrate potential applications of data obtained through high-throughput phenotyping in the fields of plant biology and predictive plant breeding.The study utilized temporal phenotype data derived from repetitive drone flights equipped with various sensors. These data were incorporated into a novel mixed model, providing insights into the temporal genetic effects on different genotypes/plants. Gaussian or Lorentzian peak models, as well as Functional Principal Component analysis, were employed to characterize the growth patterns of various genotypes in diverse environments. The research revealed that Temporal Effect Sizes of Quantitative Trait Loci (QTLs) influence growth differently across time points, highlighting the dynamic nature of plant development. Furthermore, the study uncovered time-dependent associations between genotypes and their environments based on temporal phenotype values.The predictive capability of temporal phenomic data was found to surpass that of genomic data in predicting complex traits in maize. However, the combination of phenomic and genomic data consistently yielded the most accurate predictions for complex traits. By analyzing drone flights at specific growth stages, the study quantified physiological traits such as senescence progression across multiple time points. This analysis led to the calculation of new traits, including days to senescence and grain filing period, providing valuable insights into plant development and growth dynamics.

Wheat is an important primary crop that nourishes billions of people worldwide. Wheat diseases, particularly Fusarium head blight (FHB) disease, often have a severe effect on wheat yield in terms of both quantity and quality posing potential threats to the health of humans and livestock. Traditional methods such as field surveys for monitoring and assessing wheat diseases are time-consuming, costly and inefficient. In recent years, remote sensing approaches, particularly aerial imaging using Unmanned Aerial Vehicles (UAV), have become invaluable tools for rapid field scouting at larger scale, as well of crop growth and health status. This study aims to investigate the potential of combining high-resolution UAV multispectral imagery with machine learning (ML) methods for the estimation of FHB disease severity. Two experimental wheat fields were established at Volga, South Dakota, USA, in 2022. The severity of FHB disease was assessed and rated in the fields; and synchronous UAV flights were conducted to collect multispectral imagery. Canopy spectral and texture features were derived from the UAV multispectral imagery and used as input variables for ML models to predict FHB disease severity levels. Both classification and regression approaches were applied to estimation FHB severity using ML models such as Random Forest, Support Vector Machine, and Deep Neural Networks. The results show that both canopy spectral and texture features are important indicators for monitoring the severity of FHB wheat disease. Furthermore, the use of UAV remote sensing, combined with ML-based modeling, is a sustainable approach for rapid and accurate detection of wheat FHB disease severity.

Unoccupied / Unmanned / Uncrewed Aerial Systems (UAS, also known as drones) are tools that can provide field-based phenotyping and phenomics derived insights into plant breeding, biology, genetics and agronomy. There are many important, yet disparate agricultural UAS activities, occurring in silos which with better communication across research, education, and extension, could create transformative change for all stakeholders. The goal of this USDA-NIFA and AG2PI supported project is to advance knowledge and activities through promoting UAS data collection, processing, analysis, and community discussions. The objectives include: 1) Encourage collaboration pertaining to best practices between university, industry, and personal stakeholders who are currently developing and using UAS tools, ranging from beginner to experts. 2) Process Genomes to Fields (G2F) datasets (2017 to 2023, and up to eleven locations with ~6TB of data) into widely accessible and usable end products the community can directly use. And 3) Institute a user-centered webpage so that contributors and interested parties can access information about UAS based HTP conveniently. To accomplish the goals of the project, a UAS Project Coordinator will work to identify existing use of UAS in agriculture user groups, listen to seasoned advice and newcomer needs, work as a liaison to conduct the sharing of wisdom, and summarize their knowledge and share personal connections across discipline, institutions, and species; making this information easier to access in the future. The project is working to ease the learning process of beginners using UAS tools, as well as sharing the advancements of experienced users on the forefront of innovation and discovery.

Phosphorus (P) is a vital macronutrient for building up essential biomolecules in plants, and its accurate quantification can guide effective crop management and increase crop growers’ profit. Traditional chemical reaction-based methods for measuring P levels in plants are destructive and complex. Hyperspectral imaging offers a real-time, non-destructive avenue for assessing crop nutrient status. While these images are rich in both spatial and spectral information, limitations in current devices and analytical algorithms have led most studies to concentrate solely on the spectral features. In this study, a novel algorithm to combine features in spatial and spectral domains is proposed and implemented to differentiate phosphorus deficiency symptoms in corn plants. At the V6 vegetative stage, leaf-level hyperspectral images from three P levels and two leaf positions were collected using the handheld proximal hyperspectral imager, LeafSpec. Spatial and spectral features that exhibit significant differences between the P treatments were generated by integrating pre-designed spatial partitions with spectral index maps. The correlation coefficient between the P content and elected spatial and spectral features was treated as the standard to further refine the mining results. The spatial and spectral joint effects showed superior ability than spectral indices in differentiating P deficiency at both leaf positions, especially for medium P and sufficient P. Related visualization maps also gave out a preliminary insight into the differences in P deficiency symptoms. This study highlights the great potential and effectiveness of combining spatial and spectral features in differentiating P levels at corn’s early vegetative stage.

Phenotyping plays a crucial role in parameterizing, calibrating, and evaluating process-based plant models, which can be used to understand and predict crop behavior under various conditions. This, in turn, allows for more informed decision-making in agriculture and provides insights into how crops may respond to changing environmental factors. Nevertheless, several potential disparities between current plant models and phenotyping methods exist, which have the potential to undermine the precision and applicability of these models. These discrepancies encompass differences in data resolution and scale (both spatial and temporal) between what is feasibly gathered during phenotyping efforts and what is required by the models. Additionally, issues related to compatibility between the traits measured during phenotyping campaigns and the parameters required for models are prevalent. Furthermore, the representation of genetic and environmental variability in both models and phenotyping data is often limited, resulting in a gap between these two components.To address these discrepancies, we are developing a new “minimum” plant model. This model has been designed with strong consideration of what data can be measured in phenotyping campaigns across multiple genotypes. While the core model remains straightforward, it has the capability to simulate essential processes to represent fundamental plant physiology. Moreover, it has the potential to be integrated into larger, more comprehensive models or parameterized to suit specific genotypes. This approach aims to bridge the gap between phenotyping and crop models, thus enhancing their effectiveness in addressing the challenges that emerge by utilizing them in broader agricultural applications.

Deep learning is a central tool in plant phenotyping. Proficiency in artificial intelligence and programming remains essential for designing a deep learning model. The reuse of deep models can be challenging for non-coding end-user such as plant biologists. Currently, very few tools facilitate collaboration between computational infrastructure, non-coding end-users, and deep learning project manager. Consequently, this limits the deployment of multi centric large scale deep learning initiatives (such as the notable exception of the Globally Wheat Challenge) in plant phenotyping. We propose the scheme of Fig. 1 to allow non-coding end-users to test existing deep learning models with a homemade software capable of running any segmentation, classification or object detection deep learning model. This software is an open source plugin coined MANINI (https://github.com/hereariim/manini) which runs under the north American initiative NAPARI (https://napari.org/). The plugin also enables the manual correction of the inference. The corrected inferences can then be used to retrain or fine-tune the model on a large-scale infrastructure (in our case the European Grid Inrastructure EGI) via the DEEPaaS API. The resulting new model is then accessible on a public repository. We illustrate the interest of this scheme on various plant phenotyping use cases.