Hyperspectral imaging (HSI) is being widely applied in plant phenotyping platforms. Some new HSI devices such as LeafSpec was introduced recently which can provide a high signal-over-noise ratio along with higher spectral and spatial resolutions. However, most of the previous image processing algorithms only calculated the averaged spectrum from the leaf, but rarely include the spatially distributed information on the leaf level. Meanwhile, different nutrient stresses could result in different color patterns on the leaf which can be used to furtherly improve the quality of plant phenotyping. This study focused on the development of a new methodology that applies spatial distribution analysis on HSI soybean leaf images. Firstly, a novel way of encoding all the leaf pixels to a new coordinate system called Natural Leaf Coordinate System (NLCS) was developed. NLCS defined the coordinates of every pixel relative to the leaf venation so that the following spatial distribution analysis could be conducted more intuitively. Second, a new nitrogen index based on NLCS called NLCS-N was developed and able to outperform the whole leaf averaged NDVI by having a better correlation with the plants' nitrogen contents, and a more significant differentiation between the nitrogen-sufficient versus the nitrogen-deficient plants.

Advances in phenotyping tools, genomic methodologies, and analytics strategies provide new tools to assess germplasm merit; however, more work is needed to integrate these systems into modern plant breeding approaches. The objective of this work is to integrate genomics and phenomics for yield prediction in maize. A panel of 830 temperate and tropical inbred lines were evaluated for their testcross performance in 2018, and a subset of 400 testcross hybrids were evaluated in 2021 and 2022. These experiments were performed in West Lafayette, IN in a randomized complete block design with two replications. Remote sensing data was collected on a near weekly basis throughout each growing season for RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data and grain yield was harvested with a plot combine. Remote sensing traits extracted include canopy cover, plot volume, plant height, and NDVI. A GBLUP genomic prediction model was used to estimate yield performance in 2018 using data collected in 2021 and 2022. Remote sensing traits were estimated at regular intervals throughout each growing season using random regression modelling. Grain yield was estimated using the genomic estimated yield and the remote sensing traits in a machine learning model. Preliminary results indicate remote sensing can improve prediction accuracy of grain yield compared to genomic prediction alone even with data only collected before flowering. Improved prediction accuracy could benefit hybrid selection, increase genetic gain, and reduce cost in a breeding program.

This study evaluates a hyperspectral imaging (HSI) technique to identify herbicide-resistant kochia (Bassia scoparia) biotypes to support weed management in cropping systems. The experiment was conducted under controlled-environment where glyphosate was applied to six different kochia populations. For each population (72 cell tray of plants), half of the plants were sprayed with Glyphosate 900 g ae ha-1 , while the other half remained an untreated control. Hyperspectral images were acquired over five time points spanning from glyphosate treatment to 15 days after treatment (DAT) using a proximal HSI system (Specim-IQ) with 204 spectral bands from 397nm to 1003nm. The average reflectances were extracted from plants that were characterized as glyphosate-resistant or-susceptible. We first analyzed the temporal variations of the spectra with and without the application of herbicide. The spectral profile exploits the advantages of temporal features in biotype discrimination. Random forest algorithms were used to classify the glyphosate-resistant and-susceptible populations, by using reflectance at optimal wavelengths (near-infrared) and various vegetation indices with high correlations with visual ratings. Based on the classification accuracy, the most important wavebands and vegetation indices were determined to classify the weed biotypes. Preliminary results show that 1) For the untreated plants, the reflectance at red-edge to near-infrared reached the highest level on 8 DAT, revealing the highest chlorophyll content in the leaves. Then, the reflectance declined until 15 DAT. 2) In contrast, strong effects of glyphosate were captured on 8 DAT for the three herbicide-susceptible populations. For the three glyphosate-resistant populations, reflectance at red-edge to near-infrared did not increase from 1 to 8 DAT, which was opposite of the controlled plants.

In natural ecosystems and low-input agriculture systems often the main source of nitrogen is biological nitrogen fixation by symbiotic coexistence with root colonizing microorganisms such as in root nodules in legumes. In return for this nutrient supply, plants allocate significant amount of photosynthetically fixed carbon (C) belowground, fueling activity and growth of the nodules. However, there is still a lack in understanding how plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. Traditional approaches based on destructive sampling make investigations of localized carbon allocation dynamics difficult. Non-destructive 3D-imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offers new perspective in analysing belowground processes on individual plants. MRI allows for repetitive measurements and quantification of root system architecture traits nodule structures while growing. PET was employed to follow the spatial distribution of leaf-supplied 11 C tracer to nodules and roots. Using Pisum sativum as model for legumes and applying nitrate as an additional N source we investigated short term C allocation dynamics in the root system. We found that the fraction of 11 C tracer arriving in the most active nodules decreased by almost 40% and remained stable between 16h and 42h after the N application. Our results highlight that the combination of MRI-PET enables deeper insights into short term C dynamics of roots and interactions with colonizing microbes. We expect that this modality has high potential for revealing mechanisms that relate to dynamic fitness traits supporting breedingprograms for future crops.

Dani C. Gafford1, Jaime Barros1

Holistic assessment of fruit quality is an essential component of producing Strawberry varieties that will succeed in the marketplace and improve consumer satisfaction. However, several key quantitative traits are notoriously slow and expensive to assess using standard procedures, namely acidity and aroma, which require titration and gas chromatography and mass spectroscopy compared to others: brix, anthocyanins, and vitamin C, which are measured by refractometer and parallelized plate reader assays. Scaling up evaluations for acidity and aroma has been difficult as the techniques require 5 and 40 mins/sample, respectively, and sample preparation is equally intense, requiring multiple trained hands working for 10-hour sessions to create the sample series for 100 entries. We evaluated the ability (R 2 , RMSE) of a handheld near infrared (NIR) spectrometer, measuring 125 wavelengths between 800 and 1600 nm, and an electronic nose, measuring the reaction of 32 electrochemical sensors that respond to various compounds in gas samples, on 4,000 diverse strawberry accessions to determine if the 5 and 40 min/sample assays can be replaced with a 1 (0.33%) sec/sample (NIR) and 2 (5%) min/sample (E-nose) assay that require no additional sample prep. We also assess the NIR's ability to predict brix, anthocyanins, and vitamin C. With these two sensors, we will be able to increase the scale of early generation evaluation from hundreds to thousands of samples in early generations, produce full datasets prior to deadlines in the breeding program, and make more reliable genetic gains for quality traits affecting marketability and consumer acceptance.

The California strawberry industry generated more than 2 billion dollars in revenue in 2020 (USDA-ERS). Strawberry breeders develop new varieties to increase productivity in the face of shifting biotic and abiotic stresses. The University of California Davis maintains a strawberry breeding program that evaluates >10,000 entries yearly to meet the demand for new improved varieties, focusing on plant productivity, fruit quality, and resistance to soil borne pathogens. One challenge to a breeding program of this scale is efficiently scoring and collecting detailed information on cultivar performance. Traits like plant size and growth rate are rarely collected. It takes a crew of 4 people 20-25 hours to score fruit count, so it is currently done once per week. Correlated traits, e.g., plant size and vigor, assessed by drone imagery could provide high-quality information and replace labor intensive assessments of phenotypic traits and yield. In 2022 we deployed drones to generate research grade imagery of nearly 10,000 entries at Wolfskill Experimental Orchard in Winters, CA and another 3,000 entries under induced disease pressure to determine the best predictors of productivity and disease severity from drone imagery. We applied image analytics tools developed by HIPHEN to extract ground coverage, plant height, biovolume and a range of visual indices from multiple sensors to assess cultivar performance. The extracted traits were then used as independent variables to predict either yield or visual disease severity. We report our initial findings, examine the successes and learnings, and propose solutions to ongoing challenges in strawberry breeding.

Unmanned aerial vehicle (UAV)-based imagery has become widely used in collecting agronomic traits, enabling a much greater volume of data to be generated in a time-series manner. As one of the cutting-edge imagery analysis tools, machine learning-based object detection provides automated techniques to analyze these imagery data. In our previous study, UAVs have been used to collect aerial photography for field trials of 233 diverse inbred lines, grown under different nitrogen treatments. Images were collected during different plant developmental stages throughout the growing season. This dataset of images has here been used in developing machine learning techniques to obtain automated tassel counts at the plot level through the season. To improve detection accuracy, we have developed an image segmentation method to remove non-tassel pixels and then feed these filtered images into machine learning algorithms. As a result, our method showed a significant improvement in the accuracy of maize tassel detection. This method can be used in future research to produce time-series counts of tassels at the plot level, and will allow for accurate estimates of flowering-related traits, such as the earliest detected flowering date and the duration of each plot's flowering period. This phenotypic data and the trait-associated genes provide new opportunities for crop improvement and to facilitate future plant breeding.

Mung bean (Vigna radiata (L.) Wilczek) is an important crop providing protein, fiber, carbohydrates, and minerals in Southeast Asia and Africa. Trifoliate leaves in mung beans are central to several plant processes like photosynthesis, light interception, early disease & pest warning signals, and overall canopy architecture. We sampled more than 5000 leaf images of the Iowa Mung bean diversity panel (IMDP) during the 2020 and 2021 growing seasons in a Randomized Complete Block Design. We recorded the phenotypic diversity, developed a regression model for the oval leaflet type, and conducted GWAS for the image extracted traits. The diversity in the morphology included leaflet type (oval or lobed), leaflet size (small, medium, large), lobed angle (shallow, deep), and vein coloration (green, purple). A universal regression model LA = b0 + b1L + b2W + b3L*W was the best at predicting the area of each ovate leaflet with an adjusted R2 of 0.97. The candidate genes Vradi01g07560, Vradi05g01240, Vradi02g05730, and Vradi03g00440 are associated with multiple traits (length, width, perimeter, and area) across the leaflets (left, terminal, and right) and would be suitable candidates for further investigation in their role in leaf development, growth, and function. Future studies will be needed to correlate the observed traits discussed here with yield or important agronomic traits for use as phenotypic or genotypic markers in marker-aided selection methods for mung bean crop improvement.

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

Michelle Cho1, Zhengbin Liu2, Dhineshkumar Thirupatthi3, Tim Parker3, Shayla Gunn4,

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.

Roots are the "hidden half" of the plants, which play an essential role in plant development by drawing water and nutrients from the soil and providing anchorage to the plants. Global warming alters soil properties that limit root development and produces drought-like conditions that reduce water and nutrient uptake by the plant roots, ultimately affecting plant growth. It is, therefore, necessary to study Root System Architecture (RSA) traits and breed plants with better root architecture capable of resisting these conditions. RSA comprises various root traits including total root length, root angle, number of lateral roots, and many other traits. Due to the challenges in phenotyping these traits, the research on RSA is lacking behind. In this project, we are studying RSA traits in 500 late-maturity soybean accessions based on germinating the seeds on germination papers, transferring them to blue blotting papers, and imaging the roots 21 days after transfer. Image analysis for the root traits will be carried out using RootNav2.0 software. These phenotypic data and SNPs selected from Illumina Infinium SoySNP50K iselect SNP Beadchip will be used to perform Genome-wide Association Studies (GWAS) using Genome Association and Prediction Integrated Tool (GAPIT) package in R to identify genomic loci associated with each trait. The results from this study will help in discovery of new QTLS or genes that play a key role in root system architecture and help in breeding novel cultivars that are resistance to changing climate.

ORCiD: https://orcid.org/0000-0002-6391-1907. High-throughput phenotyping (HTP) are cost effective platforms that provide efficient evaluation of genetic resources, promote germplasm utilization, and inform breeding efforts for development of climate-resilient crops. MultispeQ device, a rapid, cost effective, and reliable HTP tool was employed to screen 112 cowpea genotypes for photosynthetic performance under drought stress conditions in a 8× 14 alpha-lattice design during the 2020/2021 and 2021/2022 dry seasons in three agro-ecological zones in Nigeria. Drought stress was imposed at 35 DAP and relative chlorophyll content (RCC), leaf temperature differential (LTD), leaf angle, linear electron flow (LEF), Phi2 (Quantum yield of Photosystem II) before and during drought stress imposition, were measured. Data was subjected to ANOVA using a mixed model for individual and combined environment analysis. Each stress condition by year was considered as an environment, giving a total of twelve environments. Best Linear Unbiased Predictions and broad-sense heritability (H 2) were computed. Significant genotypic effects for RCC, LEF, LTD, Phi2 before and during stress conditions were recorded while genotype × environment effects were observed for all measured traits. LEF had lowest H 2 (27%) while LTD had highest H 2 (63%). High RCC and lower LTD under drought stress suggested drought tolerance and stomatal closure, reported to be associated with plant biomass under limited water conditions. Genetic variation in photosynthetic performance exists among the cowpea genotypes under drought stress. RCC, LTD and Phi2 were identified as useful traits in selection for drought resistant lines aimed at boosting crop production in a changing climate and extreme weather conditions.

Maize (Zea mays ssp. Mays) is one of the most essential cereal crops in the world. As climate changes, breeding for high nitrogen-use efficiency maize genetic materials without sacrificing the yield becomes more urgent than anytime before. Image-based high-throughput phenotyping, functioning as a key element in plant breeding efforts, is critically important and has the potential to relieve difficulties in phenotypic scoring on breeding pipelines. Numberless studies using RGB images related to biomass and agronomically important traits have mostly focused on above-ground traits. The belowground root-related traits, however, have not been intensively studied. The objective of this study is to investigate the root architecture and phenotypic properties (number of aerial roots, stem diameter, internode length, and fresh root weight) of roots of the hybrid and inbred maize lines grown under low and high nitrogen conditions. In this study, a collection of BGEM lines (n = 304 inbred and n = 197 hybrids) were planted on the field in low and high nitrogen conditions. The root samples of n = 2,100 plants were collected, and the soil around the roots was washed out for automated image-based phenotyping. The roots were imaged with the completely automated conveyor belt LemnaTec system. A high variation in root structure, stem diameter, number of aerial roots, and internode length was observed among the genotypes. Our root phenotyping pipeline and traits extracted from these images will enhance root biology and facilitate breeding for below-ground root traits.

Tillers are shoots that arise from the base of a plant. When plants tiller, they place more carbon resources into vegetative growth as opposed to their grains. Understanding the environmental and genetic factors behind tillering is hampered by lack of high-throughput phenotyping for determining plant tiller count and angle. Currently, plant tiller counts are determined through manual inspection, which is laborious and low-throughput. In this study, we introduce a PlantCV (https://plantcv.danforthcenter.org/)-based algorithm for detecting tillers. This method uses OpenCV's line detection algorithm to detect lines that correspond to the tillers of the plant. From this, tiller count and angle of growth can be inferred. We use this method on Sorghum bicolor accessions from the TERRA-REF project that were grown for two weeks, cut back, and then allowed to regrow for two weeks. Of 200 randomly chosen images, this algorithm was able to accurately count within 1 tiller of the true number of tillers for 165 images. Furthermore, we find that these Sorghum bicolor accessions appear to place less resources into their tillers in the regrowth phase.

ORCiD: [0000-0001-6507-9985 of Jinyoung Y. Barnaby], [0000-0001-9082-6583 of Scott E. Warnke] Precise assessment of large mapping populations, comprising a few thousand plants including replications (a prerequisite step for breeding) is time-consuming and labor-intensive. Furthermore, phenotyping results tend to be variable and subjective depending on who is doing the scoring. One way to overcome these limitations is by collecting more data in the form of digital images, and precisely evaluating phenotypic variation in stress severity as well as temporal progression of stress symptoms within the population through machine learning methods. 230,400 images representing temporal progression of drought stress symptoms of interspecific turfgrass hybrid mapping population were processed using Python OpenCV and NumPy packages for noise removal, edge-preserving smoothing, color space conversion, contrast enhancement, and identification mapping. Then machine learning-based algorithms and models were developed not only to quantify stress severity but also to monitor temporal progression rate of stress symptoms. Hierarchical clustering was then performed to assess genotypic variation in stress progression. Such machine learning-based high-throughput digital phenotyping platforms can significantly increase the success of quantitative trait locus mapping and candidate gene identification to develop potential molecular markers that will assist in a faster characterization of germplasm to ultimately breed for stress resilient cultivars.

Measurement of key crop physiological traits using high resolution aerial imagery with unmanned aerial systems (UASs) holds enormous potential to increase consistency and accuracy of data collected for field evaluations. Here, we demonstrate temporal corn response to fertility treatments using repeated measurements followed by an area-under-the-curve progression analysis. Radiometrically calibrated multispectral datasets were used to calculate standard vegetative indices as well as to leverage models that approximate leaf area, nitrogen content, chlorophyll content, and canopy uniformity. In addition, digital elevation models can be employed to measure relative canopy heights and spatial variability in the field. Taken together, these digital assessments allow for a researcher to have significant insight into experiment outcomes during the growing season, including the identification of relative yield potential. This approach automates and standardizes the acquisition of key phenotypes that can be used to more efficiently evaluate field trials across multi-location programs.