Climate change and harsh agricultural practices are increasing the amount of salt and heavy metals in soil, drastically decreasing the amount of arable land while simultaneously lowering crop yields. However, some plants grown in poor soil have adapted diverse mechanisms to cope with harsh environments. It has been hypothesized that the biochemical mechanisms responsible for salt tolerance overlaps with heavy metal tolerance, yet the similarities in these mechanisms are still unknown. Lessons from naturally salt and heavy metal tolerant plants can be applied to crops to increase resilience or be used in phytoremediation efforts. Here, we use the salt and heavy metal tolerant plant Cakile maritima as a model system for phytoremediation by using a large-scale multi-omics approach, combining ionomics, metabolomics, transcriptomics, and genomics, to understand the metabolic responses following NaCl and cadmium stress. We have developed an automated pipeline for tracking salinity, as well as using elemental analysis to monitor intracellular concentrations. We will perform RNA-seq to understand patterns of differential gene expression, gather a list of candidate genes, and use comparative genomics to understand the potential influence of ancient polyploidy on stress tolerance. Combining this with metabolomics will enable a fully integrated understanding of salt stress response and allow us to know if Cakile maritima is predisposed for salt stress or has a rapid stress response. Coupling this with transcriptomics will allow us to identify important pathways and neofunctionalized genes that may be specific for C. maritima stress response and be applied to crop species to enhance resilience.

Field Based High Throughput Phenotyping Enables the Discovery of Loci Linked to Senescence and Grain Filling Period ORCiD: [Alper Adak; 0000-0002-2737-8041] Keywords: Grain filling period, field-based high throughput phenotyping, days to senescence, temporal phenotype. Senescence occurs progressively over time and is variable among different genotypes. To examine the temporal and genetic variation of senescence, 280 maize hybrids and 520 maize recombinant inbred lines (RILs) grown in 2017 and 2018 were investigated. Hybrids were grown in late and optimal planting trials; RILs were grown in irrigated and non-irrigated trials, both based on range-row design with two replications. Two types of Unmanned aerial systems (UAS, also known as UAV or drones) were flown over the germplasm between 14 and 20 times respectively. Temporal senescence of each row-plot in hybrids and RILs was scored visually according to percentile scoring using four to five rectified drone images between ~90 and ~130 days after planting. A mechanistic growth model was fit to each genotype using the temporal senescence scores, resulting in 0.94 and 0.97 R 2 for hybrids and RILs. Days to senescence (DTSE) and grain filling period (GFP) were calculated for each row plot using the developed mechanistic growth model. To predict the genotypic value for each RIL and hybrid, a mixed model with three-way interaction model (Genotype*Flight*Environment) was then run. Correlation was calculated ~0.84 and ~0.88 between grain yield and GFP and DTSE in hybrids. A major quantitative trait locus was also discovered on chromosome 1 (295.5 to 296.8 kb; 15% explained) linked to GFP in RILs. GFP is known to be physiologically important, UAS provided an easily scalable measure which can greatly increase the evaluation of variation in the field.

ORCiD: [0000-0002-7283-3357] Plant Scientists are striving to improve crop response to abiotic stress under adverse environmental conditions. Many bio-physical , biochemical, and physiological traits are difficult to quantify due to the low throughput and destructive nature for their measurements. This study aims to characterize biophysical and physiological traits of maize plants using RGB and hyperspectral imaging in greenhouse condition. Single hybrid Maize genotype with four different treatment combination of water and nitrogen were tested. Plants were imaged, harvested, and measured at several growth stages range from V6 to R5 stages. Images were analyzed and correlation was established between manually measured plant traits and pixel level information extracted from the plants. RGB images are processed to determine projected plant area which are correlated with destructively measured plant shoot fresh weight, dry weight, and biomass area. Hyperspectral images are processed to extract plant leaf reflectance and correlated with leaf nitrogen/chlorophyll content. PLSR models are calibrated to estimate corn leaf nitrogen/chlorophyll content from image-generated hyperspectral data, as well as the leaf hyperspectral data from a handheld ASD spectrometer and their performance will be compared. Biological science, computer vision, mathematics and engineering can be integrated as a holistic approach for quantifying the overall growth, development, and response of maize plants under differential nitrogen rates.

A document by Sierra Young. Click on the document to view its contents.

Predictive biology is the ability to predict a biological outcome from known inputs (and vice versa). Complex and urgent problems, such as climate change and a growing global population, requires a better grasp of predictive biology approaches. However, predictive biology requires a deep understanding of the genome-to-phenome relationship within an organism. The field of genomics has accelerated rapidly in the last few decades with technological advances that have helped reduced the costs of genomics research and easy-to-use computational tools. Phenomics technologies has not advanced at the same rate. Many phenotyping systems are expensive to own, require special training to use, focus on narrow areas of research, and produce large amounts of data with no standards of storage and documentation. We propose a design philosophy for high-throughput phenotyping systems called the OPEN Series. This philosophy focuses on systems that use off-the-shelf commercial products and open-source software to make high quality phenotyping systems efficiently and for use by general users. In addition, the OPEN Series focuses on integrating cloud-based image processing through the NSF-funded cyberinfrastructure CyVerse, thus allowing users to share and process data remotely. We've worked to integrate this philosophy into our own phenotyping systems, OPEN leaf and OPEN root, to great success. We hope to export our work in creating accessible and affordable phenotyping system to labs across the globe to accelerate our understanding of the genome-to-phenome relationship for predictive biology.

Environmental stressors play a major role in determining plant phenotypic traits over the growing season. Nitrogen, a critical nutrient for plant growth, imposes serious stresses when applied in insufficient or excessive quantities in crop fields, which can directly reduce yield and the grower's profits. Due to significant variations in developmental rates and nitrogen needs across rice genotypes and environments, field phenotyping can become a challenging task. Non-linear manifold learning approaches were evaluated to account for variation in developmental rate using multispectral, multitemporal imagery acquired by an unmanned aerial vehicle (UAV). Imagery (13-16 flyovers per plot) and ground-truth data (including biomass nitrogen content, leaf chlorophyll content, and yield) were collected on 2,345 rice research plots during the 2021 and 2022 growing seasons. To increase the size of the training set, we also evaluated inclusion of similarly sized tiles sampled from commercial row-rice fields imaged between 2019 and 2022. We compared several supervised machine learning approaches with unsupervised strategies based on manifold learning to evaluate the hypothesis that model architectures which account for variation in developmental trajectories improve prediction of yield variation in response to nitrogen addition. This work will contribute to improved nitrogen rate recommendations using UAV-acquired imagery. Optimized nitrogen prescriptions ensure satisfying the plants' nitrogen needs and contribute to reduced environment impacts and production costs.

From preventing scurvy to being part of religious rituals, citrus are intrinsically connected to human health and perception. From tiny mandarins to head-sized pummelos, citrus capability of hybridization provides a vastly diverse array of fruit sizes and shapes, which in turn corresponds to a diversity of flavors and aromas. These sensory qualities are tightly linked to oil glands in the citrus skin. The oil glands are also key to understanding fruit development, and the essential oils contained by them are fundamental in the food and perfume industries. We study the shape of citrus based on 3D X-ray CT scan reconstruction of 163 different citrus samples comprising 58 different species and cultivars, including samples of all fundamental citrus species. First, using the power of X-rays and image processing, we are able to compare and contrast size ratios between different tissues, such as the size of the skin compared to the rind or the flesh. Second, we model the fruit shape as an ellipsoidal surface, and later we study and infer possible oil gland distributions on this surface using principles of directional statistics. We finally compare and contrast these overall fruit shape models along their gland distributions across different citrus species. This morphological modeling will allow us later to link genotype with phenotype, furthering our insight on how the physical shape is genetically specified in DNA.

Stomatal conductance (SC) was utilized to indicate the rate of gas and water exchange through stomata on the leaf surface. When crops are experiencing water stress during the daytime, their stomata will close to prevent water loss. However, the crop will also receive less CO2 for photosynthesis under this condition. Consequently, accurate and efficient SC prediction can improve irrigation efficiency, particularly in the present day when water is scarce. The common way to estimate SC nowadays is to make predictions using other variables that can be measured quickly, such as conventional regression analysis. The limitation of conventional regression analysis is that it does not account for changes in SC when crops are subjected to both prolonged drought and high temperature stress. We intend to use time series prediction techniques, such as Long Short-Term Memory (LSTM), to determine the correlation between variables at different time scales. The objective of this study is to (1) Investigating the relationship between SC and persistent weather patterns. (2) Predict SC based on continuously collected soil moisture, plant canopy temperature and weather information. In this study, we measured the SC of soybean and maize by using a handheld leaf porometer. This was then compared to short-term historical crop SC predictions based on canopy temperature, soil moisture, and weather conditions. Autoregressive Integrated Moving Average (ARIMA) and LSTM based on Recurrent neural network (RNN) are used to predict SC, and the results are compared with the conventional modeling method. More details will be presented at the NAPPN conference.

ORCiD: [0000-0002-9614-5436] Keywords: unoccupied aerial vehicles (UAV), maize, plant height, growth rate, genome-wide association study (GWAS), canopy cover, environment, genotype-by-environment interaction Body Text: Plant height is used in many breeding programs for assessing plant health, predicting yield, or monitoring growth rates which can be used in identifying superior hybrids or evaluating abiotic stress factors. This has often been measured at a single time point when plants have reached their terminal height for the season. Collection of plant height using unoccupied aerial vehicles (UAVs) is faster, allowing for measurements throughout the growing season which could facilitate a better understanding of plant-environment interaction and responses. Plant height data was collected weekly for a panel of ~500 diverse lines over four growing seasons. The variation in plant height throughout the season was found to be explained by genotype, year, and genotype by year interactions. However, the relative contributions of these different sources of variation fluctuated throughout development. Genome-wide association studies of plant height and growth rate revealed many significant SNPs associated with plant height and growth rate at different parts of the growing season that would not be identified by terminal height alone. When comparing growth rates from plant height to growth rates estimated from another morphological characteristic, canopy cover, we found that canopy cover appears to be more affected by environment than plant height. This potentially makes canopy cover more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth.

William LaVoy1 , Limeng Xie1, Suxing Liu1, Alexander Bucksch1231Department of Plant Biology, University of Georgia, Athens, GA 306022Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 306023Institute of Bioinformatics, University of Georgia, Athens, GA 30602

Digital twins are a promising approach to create a detailed data foundation providing new insights for breeders and farmers. Using digital twins can make harvest predictions more precise or help detect anomalies (like e.g. diseases or effects of stress) early on. Trees and orchards in particular could benefit from accurate plant data, including plant morphology, objective data like crop size/count per tree, etc. Using a drone-mounted aerial camera we automatically navigate a cherry orchard and generate a series of 2D images as the basis for further processing. Since the processing and amount of data that can be stored on-board a drone is very limited, an approach was chosen to stream the data to the processing computer utilizing a 5G campus network. 3D color point clouds are generated from the 2D images and using AI-based segmentation and image processing each tree is separated into organs, and the tree's morphology is deconstructed. The different plant organs (trunk/branch, leaf, cherry) are then individually digitally modelled, directly generating interpretable values like volume or surface area for each individual plant organ.

Reducing atmospheric CO 2 is essential to mitigating the effects of climate change. Marine macroalgae has garnered interest as a potential carbon sink to sequester atmospheric CO 2. Efforts to improve macroalgae for use as a carbon sink are just beginning, and understanding the phenotypic diversity of macroalgae is a critical part of identifying favorable characteristics for a marine carbon sink. In this study, we surveyed the natural variation of the marine macroalgae, Saccharina latissima (sugar kelp), from eight sites along the western coast of Iceland: three in Breiđafjörđur, four in Faxaflói, and one in the Atlantic Ocean south of Reykjanes. All collected sugar kelp samples were in the non-reproductive stage, and environmental data was collected at each site. All collected sugar kelp samples were phenotyped manually and through image analysis. Chlorophyll content, carbon and nitrogen content, and relative carbohydrate contents were also quantified. Strong correlations were observed between many morphological phenotypes and with environmental variables such as air temperature. While phenotypic differences between sampling locations were observed in some individual traits like blade length and chlorophyll content, principal component analysis did not reveal significant grouping of individuals by sampling location based on phenotype. These results suggest that phenotypic variation in sugar kelp populations across the Western Icelandic range is not strongly influenced by changes in environmental variation. These results are consistent with natural variation studies conducted in sugar kelp samples from New England, United States and indicate that sugar kelp can be a favorable choice for carbon sequestration across multiple environments.

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. The development of analytical methods for improved estimation and reduced manual inspection are needed to better characterize the effects of genetics and the environment in key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times in the season, together with ground-truth data over thousands miscanthus accessions to determine flowering time, culm height, and biomass yield traits. We compared the performance of Convolutional Neural Network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates (3D-spatio-temporal) architectures to evaluate the value of persistent monitoring and the type of features to predict the traits. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height and biomass production was improved through use of 3D-spatio-temporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatio-temporal analyses compared to the best 2D-spatial architectures manifested in up to: 23 % improvements in R 2 and 20 % reduction in mean absolute error (MAE). The integration of

Franklin Heng1, Jason Belanger1, Max Horowitz1, Keely Brown1, Chris Payne1, Rishi R. Masalia1

Sara Tirado Tolosa1