We present CherrySet, a comprehensive dataset comprising observations of three cherry trees throughout an entire vegetation period. This publication is part of the For5G: Digital Twin project, where we aim to develop an end-to-end pipeline for the creation of digital twins in horticulture, from data acquisition up to end-user applications. For this purpose, we have developed a methodology for precise scanning and capturing of high-resolution image data of trees using UAV technology. Now we are releasing the dataset and preliminary findings of the 2023 season to encourage fellow researchers to leverage the data for their own research and contribute to the scientific community.CherrySet encompasses a collection of 2D images covering three sweet cherry trees at 12 distinct time points, spanning from dormancy in March through blooming and growth until harvest in July 2023. In addition to the image data, CherrySet offers manually recorded ground truth information obtained from reference branches throughout the growing season, which includes comprehensive bud, blossoms and fruit counts during all vegetation phases, as well as the total number of cherries gathered at harvest. The combination of visual and ground truth trait data allows insights into intra-seasonal analysis of tree and fruit development, providing valuable data for conventional or AI based follow-up research.We are committed to refining our image acquisition pipeline and intend to provide corresponding data for these trees throughout the 2024 season. This expansion will open up opportunities for inter-seasonal research.

Photogrammetry is the science of obtaining a 3D scan of an object. Through this process, reliable information about the physical object's complex structure can be obtained, studied and analysed. A low-cost Structure from Motion (SfM) technique can be used to create 3D models using multiple 2D images from different viewpoints. A point cloud is a widely used 3D data form, which can be produced by depth sensors, such as LIDARs and RGB-D cameras. However, the cost of such scanners can be prohibitive, putting photogrammetry out of reach for many researchers and practitioners in the agriculture industry. We are developing a low-cost close-range photogrammetry rig that could be a beneficial tool for agronomists, plant scientists, and breeders. Our imaging system utilizes the Raspberry Pi to capture images with multiple cameras, and a commercial rotatory table to get images from different viewpoints. We discuss the development of extracting quantitative trait indices in wheat in order to automatically characterize planophile versus erectophile canopy architectures. Moving forward, we plan to use our photogrammetry rig for a variety of applications such as growth monitoring and extracting plant traits such as number of leaves, stem height, leaf length, leaf width, leaf area, and canopy volume. We also plan on developing bespoke, plug-and-play systems that are tailored to the specific needs of a researcher and can be operated with minimal expertise.

Plants store carbon as oil in their seeds, which is a resource that needs to be mobilized in order to germinate. Camelina sativa is a prospective biofuel crop which has the potential to be used as a cover crop in corn and soy growing regions. Camelina plants have been engineered to produce medium-chain fatty acids useful in the production of biofuels, lubricants, and other products. To test suitability of these modified Camelina lines for growth in cool climates such as those found in the midwestern US, we performed germination tests on seeds in growth chambers at temperatures similar to those encountered in early season in corn-growing regions (6°C, 15°C, and 22°C). Controls were the untransformed background Suneson genotype, as well as plants from the far northern and southern ranges of C. sativa. Germinating seedlings were imaged using Raspberry Pis, and analyzed with PlantCV for the number of plants and the cotyledon area. To determine effects of seed fatty acid content later in the growth cycle, seedlings were transplanted and grown at 22°C until seed set. These plants were imaged every 20 days to track height, width, area, and solidity using PlantCV to analyze the data.

Weed competition with crops is the number one cause of yield loss worldwide. Plant breeders combat this issue by breeding herbicide tolerant varieties. The Aggressor (Group1) and Beyond (Group 2) herbicides are commonly used to control weeds in the Pacific Northwest. Many weed species are developing tolerance to group 1 and 2 herbicides. Metribuzin is a group 5 herbicide that is labeled for use in wheat, but application results in severe plant injury. Release of winter wheat variety tolerant to Metribuzin would provide producers an alternate mode of action to control weeds. Historically breeders visually rate injury in breeding plots, these ratings can be variable and subject to individual bias. This study aims to improve the accuracy and efficiency of selecting herbicide tolerant lines in a breeding program by utilizing a drone mounted multispectral sensor. Multispectral data was collected on paired rows and advanced generation lines grown in yield trails. The vegetative indices calculated from the wavelengths measured by the multispectral sensor in this study include NDVI, NDRE, TCARI, NWI, and MTVI. Visual injury scores, plant height, and yield were also measured. Correlations between reflectance indices and yield were stronger than correlations between visual injury ratings and yield. There were also moderate to strong correlations observed between visual injury rating and vegetative indices as well as plant height difference and vegetative indices. The results of this study suggest that multispectral analysis on the plot level is an accurate indicator of herbicide injury in winter wheat.

Maximizing crop yield while conserving resources is a pressing challenge in modern agriculture. Maize (Zea mays L. ), a staple crop worldwide, relies heavily on photosynthesis, making radiation interception a pivotal factor in crop growth and yield. This study presents a novel approach to improve maize crop productivity by harnessing the principles of phyllotaxy and optimizing planting patterns to efficiently intercept solar radiation. Through the enhanced 3D maize model generation algorithm, the simulation incorporates critical factors such as curved surface leaf area, leaf arrangement, plant spacing, and solar angles, allowing us to quantify the radiation intercepted by the maize canopy. We investigate the most efficient phyllotaxy for individual plants and evaluate a range of planting patterns and their impact on radiation interception at various growth stages. Simulation results reveal that optimizing planting patterns based on phyllotactic principles can substantially enhance radiation capture compared to traditional planting methods. In conclusion, our research signifies a significant step towards harnessing the power of plant arrangement and optimizing planting strategies, including the use of an enhanced 3D maize model, to maximize radiation interception in maize crops. The optimal phyllotaxis and planting patterns establish a distinct phenological target for breeders. These findings hold promise for the development of more resilient and productive agricultural systems in an era of growing global food demand and resource constraints.

Breeding for improved, reliable cultivars despite growing environment irregularity can be challenging. Unmanned aircraft systems (UAS) are a popular high-throughput phenotyping technology that has been shown to help understand the mechanisms associated with crop productivity and create a potential for improved breeding strategy by providing unique insight into environmental response and cultivar productivity. Spectral reflectance indices (SRI), including both vegetation and water indices like NDVI, NDRE, and NWI were used to evaluate 11,593 Washington State University winter wheat breeding plots between 2019 and 2022. SRIs were then used with genomic data in univariate and multivariate gBLUP model predictions for grain yield. Prediction accuracy was evaluated on a leave-one-year-out validation strategy. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but minimally when considering the computationally more efficient base model. In univariate models, when test year NDVI data was used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limitations of SRI and its use in genomic selection, especially when dealing with large breeding populations across environmental extremes. A significant application for the technology can be found in early season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.

Nitrogen is a crucial nutrient for soybean growth and production. Enhancing soybean crop yield is essential to address the growing challenge of ensuring food security. While nodulation and nitrogen fixation reduce the need for additional nitrogen, the presence of actively fixing nodules, distinguished by their pink or red color, ensures effective nitrogen fixation and yield improvement in soybeans. Changes in global climate led to shifts in soil characteristics that restrict root growth and create conditions resembling drought, diminishing the capacity of plant roots to absorb water and nutrients, consequently impacting overall plant growth as well as nodulation related traits. In this research, we’re examining nodulation related traits across 500 late maturing soybean accessions. We germinated seeds on germination paper and transferred them to blue blotting papers and captured root images after 21 days. Nodulation traits such as Nodules number and area will be evaluated using the Soybean Nodule Acquisition Pipeline (SNAP). Statistical assessments including population structure, kinship matrix, and principal component analysis will be measured using R. We’ll utilize both phenotypic data and SNPs from Illumina Infinium SoySNP50K iselect SNP Beadchip for Genome-wide Association Studies (GWAS) using the GAPIT package in R. This study aims to identify genetic regions linked to nodulation traits, potentially uncovering valuable QTLs or genes for enhancing nodulation related traits.

Climate change poses a significant threat to agricultural systems, with drought becoming increasingly prevalent in the Canadian prairies. This study addresses the urgent need to enhance crop resilience, focusing on Brassica carinata, a promising industrial feedstock crop used for the production of biofuels. Our research aims to comprehensively evaluate drought adaptive capacity in B. carinata through a combination of physiological and digital phenotyping methods. Under controlled conditions, we utilized a high-throughput phenotyping platform, the Plantarray system, to screen B. carinata germplasm. This system facilitated precise measurements of physiological traits, soil conditions, and atmospheric parameters, enabling the assessment of drought response. Concurrently, we conducted a field phenotyping experiment with 47 B. carinata Nested Association Mapping (NAM) founder lines and two B. napus checks, under irrigated and non-irrigated conditions. Aerial imagery obtained through Unmanned Aerial Vehicles (UAVs), complemented by phenological observations and manually recorded phenotypic data, was systematically gathered. Digital phenotypes extracted from aerial images are analyzed to identify a digital phenotype(s) for drought tolerance. Our study also explores the correlation between indoor physiological data and field performance of B. carinata lines, in an effort to identify parameters that can serve as reliable predictors of seed yield under drought stress. Overall, we believe this research provides valuable insights for enhancing crop resilience to drought.

Chenopodium quinoa is an important crop known for its salt tolerance. Salinity is an osmotic stress and ions accumulation in the root zone causes a reduction in soil water availability, affecting the uptake of essential nutrients, changing seed composition, and reducing biomass. Hence, the need for high-yield crops in poor soils. This research examines the effect of salt stress on quinoa photosynthetic efficiency and salt bladder development. Sensitive and tolerant quinoa lines were examined under salt stress conditions when a concentration of 155mM NaCl was applied. Soil conductivity was monitored for salt stress during the experiment. At approximately two months old, CropReporter images were taken and analyzed using PlantCV to estimate photosystem II efficiency, non-photochemical quenching (NPQ), chlorophyll content, and anthocyanin content. The analysis showed that the salt treatment did not negatively affect the plant photosynthetic efficiency (no changes in Fv/Fm, NPQ, Fq’/Fm’) but leaf area and chlorophyll content was statistically negatively affected by the treatment when comparing genotypes. Live tissue was also analyzed using reflection and fluorescence confocal microscopy, where epidermal salt bladders images were acquired, visualized and analyzed in 3-D and the salt tolerant line showed bigger bladder volumes compared with control conditions. A more high-throughput approach using PlantCV, an open-source image analysis software package targeted for plant phenotyping. This software helped count epidermal salt bladders using stereoscope images. A comprehensive understanding of the quinoa salt tolerant mechanisms by employing multidisciplinary approaches is necessary for their effective incorporation into salt-sensitive crops for better crop yields under stressful environments.

Plant phenology and phenotype prediction using remote sensing data is increasingly gaining attention in order to enhance agricultural productivity. This work aims to generate synthetic forestry images that satisfy a specific phenotypic attribute, viz. canopy greenness. We harness a Generative Adversarial Network (GAN) to generate biologically plausible and phenotypically stable forestry images conditioned on the greenness of vegetation over a specific region of interest (describing a particular vegetation type in a mixed forest). The training data is based on the automated digital camera imagery captured by the National Ecological Observatory Network (NEON) and processed by the PhenoCam Network. This method helps render the appearance of forest sites specific to a greenness value. Further, synthetic images are utilized to predict another phenotypic attribute, viz., redness of plants. The Structural SIMilarity (SSIM) index is used to assess the quality of the synthetic images and their greenness and redness indices are compared against that of the original images using Root Mean Squared Error (RMSE) to evaluate the accuracy and integrity. The generalizability and scalability of our proposed GAN model is determined by effectively transforming it to generate synthetic images for other forest sites and vegetation types. From a broader perspective, this technique could be used to visualize forestry based on different phenotypic attributes in the context of various environmental parameters. This work provides a useful step in leveraging generative AI principles from pattern recognition and computer vision for plant phenological research.

Plant breeding programs demand efficient and accurate methods for crop phenotyping, especially for economically important crops like soybeans. Traditional manual assessment methods are labor-intensive and prone to errors. The emerging field of phenomics leverages advanced technologies, including high-resolution satellite and drone imagery, to monitor crops in a time, cost and labor efficient way. Drones offer localized, high-resolution data but have limitations in coverage and operator skills. In contrast, high-resolution satellite imagery provides broad-scale views of the vegetation with increasing improvements in spatial and temporal resolution.Our study investigates the potential of high-resolution satellite imagery as an alternative to drone imagery for assessing soybean maturity and monitoring the crop condition in a small plot breeding program. We compare the efficiency of these two technologies and we explore the utilization of various vegetation indices (VIs) derived from satellite-based multispectral imaging sensors for maturity estimation, indirect assessment of essential plant traits and identification of the growing patterns of different maturity groups.Our findings reveal the promise of high-resolution satellite imagery as a valuable tool in soybean phenotyping, addressing the spatial scale challenges in breeding programs. With advances in spatial resolution, satellites can provide detailed insights into crop health, productivity, and resource management. This research contributes to the evolution of precision agriculture, offering a cost-effective and scalable solution for monitoring soybean maturity and enhancing crop adaptability and yield.

Metamorphic core complexes (MCC) provide a rare glimpse into thermomechanical processes in the lithosphere and play a substantial role in the evolution of the crust. The North American Cordillera contains a northwest trending line of MCCs, which have been extensively studied using bedrock thermochronology and modelling approaches to better understand extensional processes related to Cordilleran collapse. While these studies have proposed a wide variety of models to explain the timing and mechanism behind MCC formation, few have considered the syn-deformational basin record, which preserves a unique archive of sediment sources in adjacent MCC highlands. This study focuses on the Deer Lodge Valley, located in the hanging-wall of the Anaconda MCC. We utilize detrital zircon (U-Pb)-(U-Th)/He double dating in the context of stratigraphic and sedimentologic analyses, and HeFTy time-temperature modelling to reconstruct basin evolution. Stratigraphic analysis shows that the basin was dominated by deposition of coalescing alluvial fans, with sediment sourced directly from the footwall of the detachment fault. U-Pb maximum depositional ages indicate late Paleocene to early Eocene proximal basin sedimentation. (U-Th)/He analyses from U-Pb dated zircons range from 194-32 Ma; >70% of dates are Eocene. Preliminary HeFTy modelling shows a period of rapid cooling between 65-55 Ma, which is supported by short (<10 Myr) sediment lag times and inferred rapid exhumation in the MCC. Our findings support a link between MCC exhumation and basin formation. They further depict a potentially earlier period of MCC exhumation than previous work has proposed, indicating an earlier onset of extension in western Montana.

Maize is pivotal in supporting global agriculture and addressing food security challenges. To better understand the genetic factors that underpin maize growth, quantitative phenotyping of traits is essential. Root systems are challenging to phenotype given their below-ground, soil-bound nature. In addition, manual annotations of root images are tedious and can lead to inaccuracies and inconsistencies between individuals, resulting in data discrepancies. To address these issues we have developed an automated phenotyping pipeline utilizing Root Painter , Rhizovision , and R for maize root image analysis and efficient extraction of phenotypic data. This pipeline was tested on images of field-grown maize crown root systems (stages V6-V8) from the Wisconsin Diversity panel. By minimizing user input and increasing automation, these tools improve the consistency and accuracy of data metrics. Root Painter, a segmentation application based on U-Net with a user-friendly interface, specializes in identifying roots and nodes. 123 images were annotated in RootPainter's interface for training. Resulting in precise differentiation between roots and non-root structures, enabling unsupervised crown root phenotyping. Finally, these segmented images were subsequently processed using Rhizovision's batch image processor, extracting numerous key root traits, including total root length, network area, and volume. The output from Rhizovision was then analyzed using an R script, incorporating statistical and visualization packages. Comparing the results obtained from our automated phenotyping pipeline with manually measured root systems demonstrated increased accuracy and consistency across researchers. This integrated pipeline saves user time and reduces costs by harnessing open-source maize phenotyping software and robust data analysis techniques.

A major challenge to community code development and management is the testing and validation of public contributions. The community-developed GFDL Finite-Volume Cubed Sphere Dynamical Core (FV3) is no exception:  automated testing of contributions made to the FV3 public repository is paramount for ensuring the integrity of the many earth-system models and forecasting applications using FV3 as a dynamical core.  A build and test system for the FV3 dynamical core was developed for internal testing on NOAA Research and Development High Performance Computing Systems (RDHPCS).  We have designed a continuous integration (CI) approach for the FV3 dynamical core Github repository that uses a cloud-based platform to perform automated compilation and reproducibility testing to validate community code contributions. A combination of NOAA RDHPCS Parallel Works virtual machines and containers developed at GFDL are used to compile and test code on the cloud efficiently.   We will also discuss how we adapted the FV3 tests for automated CI.

This research aims to address the limitations of current building codes and regulations for coastal flood hazards in the United States. The current codes only consider the 100-year flood hazard, which is not adequate for areas that are prone to more severe floods. The research presents a new methodology that considers longer return period floods by using data from flood insurance studies (FIS), statistical models and best practices to provide guidance for architects and engineers when designing buildings in coastal high hazard areas. The National Flood Insurance Program (NFIP) insurance policies and building codes require structures in coastal high hazard areas to be elevated above the design flood elevation (DFE) without using fill. However, the current elevation procedures only take into account the 100-year base flood elevation with minimal guidance for longer return period floods, which is of particular concern for critical facilities and buildings with a longer design life such as institutional buildings. The methodology proposed in this research uses stillwater elevations (SWEL) from FIS and statistical models to extrapolate flood elevations associated with longer return periods. The FIS data is fitted using the Gumbel extreme value distribution, which results in an equation that can be used for extrapolating flood elevations beyond code requirements and current best practices. The results are evaluated using R 2 values, differences in projected elevations and known elevations for the same return period, and normalized data for the 100-year SWELs. It's important to note that the results of this research are not intended to be integrated into the current codes or policy regulations in the United States. Instead, it's intended to provide generalized guidance to aid practitioners in decision making by consolidating current codes, best practices, and characteristics of the changing coastal environment. This research aims to provide architects and engineers with a better understanding of the potential flood hazards in coastal areas and help them design buildings that are more resilient to these hazards. stillwater elevations (SWEL), design flood elevation (DFE), National Flood Insurance Program (NFIP), base flood elevation (BFE), Gumbel extreme value distribution, flood insurance studies (FIS)

Though precise, most LiDARs are vulnerable to position and pointing errors as deviations from the expected principal axis lead to projection errors on target. While fidelity of location/pointing solutions can be high, determination of uncertainty remains relatively limited. As a result, NASA’s 2021 Surface Topography and Vegetation Incubation Study Report lists vertical (horizontal, geolocation) accuracy as an associated parameter for all (most) identified Science and Application Knowledge Gaps, and identifies maturation of Uncertainty Quantification (UQ) methodologies on the STV Roadmap for this decade. The presented generalized Polynomial Chaos Expansion (gPCE) based method has wide ranging applicability to improve positioning, geolocation uncertainty estimates for all STV disciplines, and is extended from the bare earth to the bathymetric lidar use case, adding complexity introduced by entry angle, wave structure, and sub-surface roughness. This research addresses knowledge gaps in bathy-LiDAR measurement uncertainty through a more complete description of total aggregated uncertainties, from system level to geolocation, by applying a gPCE-UQ approach. Currently, the standard approach is the calculation of the Total Propagated Uncertainty, which is often plagued by simplifying approximations (e.g. strictly Gaussian uncertainty sources) and ignored covariances. gPCE intrinsically accounts for covariance between variables to determine uncertainty in a measurement, without manually constructing a covariance matrix, through a surrogate model of system response. Additionally, gPCE allows arbitrarily high order uncertainty estimates (limited only by the one-time computational cost of computing gPCE coefficients), accurate representation of non-Gaussian sources of error (e.g. wave height energy distributions), and direct integration of measurement requirements into the design of LiDAR systems, by trivializing the computation of global sensitivity analysis.

Key Points:Lagrangian-based HYSPLIT modelling system used to estimate volcanic ash particle trajectories.HYSPLIT simulation took place before and after the massive eruption on 15th January 2022 (termed as pre-caldera and post-caldera respectively in Section 5)Volcanic ash particle deposition and volcanic ash particle position simulated using HYSPLIT for the HTHH submarine volcano massive eruption event.AbstractVolcano-seismic signals such as long-period (LP) events and tremors are important indicators for volcanic activity and unrest. Explosive volcanic eruptions are stunning phenomena that influence the Earth’s natural systems and climate in a variety of ways. This paper discusses the mid-week January 2022 eruption of the HTHH submarine volcano, especially on 15th January an event with many impacts in the region (dynamic, chemical, climate breakdown). Given the potential for a volcanic eruption to affect climate, the oceanic system, or climate variability, consistent and understandable modelling of these exceptional events is critical.The main objective was to determine the volcanic effects in our atmospheric boundary layer (ABL) during the multiple eruptive events occurred on January 2022 at HTHH. Our discussion also contributes to understanding the underlying Earth system dynamics triggered by cataclysmic volcanic eruptions. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model system developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Laboratory was used to deliberate the effects caused by the multiple eruptions of HTHH on mid-week of January 2022. Our modelling results include model trajectories at different frequency levels, volcanic ash deposition and ash particle position from the series of multiple eruption events of submarine volcano HTHH in the mid-week of January 2022.

Geomagnetic storms are primarily driven by stream interaction regions (SIRs) and coronal mass ejections (CMEs). Since SIR and CME storms have different solar wind and magnetic field characteristics, the magnetospheric response may vary accordingly. Using FAST/TEAMS data, we investigate the variation of ionospheric O+ and H+ outflow as a function of geomagnetic storm phase during SIR and CME magnetic storms. The effects of storm size and solar EUV flux, including solar cycle and seasonal effects, on storm time ionospheric outflow, are also investigated. The results show that for both CME and SIR storms, the O+ and H+ fluence peaks during the main phase, and then declines in the recovery phase. However, for CME storms, there is also significant increase during the initial phase. Because the outflow starts during the initial phase in CME storms, there is time for the O+ to reach the plasma sheet before the start of the main phase. Since plasma is convected into the ring current from the plasma sheet during the main phase, this may explain why more O+ is observed in the ring current during CME storms than during SIR storms. We also find that outflow fluence is higher for large storms than moderate storms and is higher during solar maximum than solar minimum.