Urban agriculture has been broadly acknowledged for its potential to reduce carbon emissions, increase food security, and improve economic growth in some of the most vulnerable communities in the United States. Collard (B.oleracea var. viridis) is a diploid leafy green, grown on urban farms and community gardens across the country, including the St. Louis Metro region. Beyond their nutritional importance, collards provide urban and commercial agronomic systems with a plethora of important ecosystem services. They scavenge nitrogen and available resources, suppress weeds, and act as a biofumigant to control soil-borne pests and pathogens. Recently, The Heirloom Collard Project characterized the above-ground growth habits of 18 landrace collard varieties across 250 organic gardens and farms. Little work has been published to investigate collard root system architecture, which influences both quality traits and ecosystem services that contribute to sustainable crop production. The objectives of this research are to 1) quantify root spatial and temporal diversity across 18 landrace collard varieties, and 2) evaluate the relationship between root phenotype and urban farmer crowd-sourced data for key traits such as germination rate, disease resistance, vigor, yield, flavor, and winter hardiness. This work will lead to the development of a participatory framework for urban farmers and chefs to select varieties with improved root architecture based on regional needs.

Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health. We developed a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a corn field across multiple diurnal cycles throughout a growing season. Using the polarization data, we present preliminary results demonstrating the potential to use polarization to de-couple light reflected from the surface versus light scattered from the tissues, thus enabling time of day, solar incidence angle, and viewing angle to be reduced as confounding factors for the spectral measurement. We present two approaches for polarization correction of our image data. The first is by using ground truth Normalized Difference Vegetation Index (NDVI) with linear regression and convolutional neural networks to train a deep learning model capable of compensating for the leaf normal relative to the camera and sun angle. The second approach involves using a recently constructed instrument which fits a scattering model of corn leaves by measuring the Bidirectional Reflectance Distribution Function (BRDF). This function models the behavior of light reflected off a leaf relative to its spectrum, polarization, and angle of incidence. Incorporating this model with data collected by the HIP, we estimate that the system will be able to distinguish leaves with surface normals facing towards the camera from leaves facing away from the camera. Preliminary results demonstrate a promising solution to reduce confounding factors in high-throughput systems for applications in plant phenomics and remote sensing.

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

Using magnetic resonance imaging (MRI), our established root phenotyping platform (van Dusschoten et al., 2016) can visualize and analyze plant roots in natural soil nondestructively (Pflugfelder et al., 2017). Using plant pots with 9 cm diameter and 30cm height, a root system can be scanned within 1h while roots down to diameters of 300µm can be detected and analyzed using our in-house root extraction software NMRooting (van Dusschoten et al., 2016). Thanks to automation with a pick-and-place robot the platform routinely achieves a throughput of 24 plants per day. All these values, however, are based on compromises between imaging speed and quality. In our system, the root detection limit is determined by the signal to noise ratio (SNR) of our images. The SNR can be increased by using smaller plant pots or by increasing the imaging time. In this contribution we investigate the potential gain in the root detection limit when sacrificing plant throughput in favor of image quality. We acquired low noise root images using repeated signal averaging during the measurement process. Using this approach, the root detection limit could be lowered, visualizing roots not detected by the standard imaging protocol.

ORCiD: https://orcid.org/0000-0001-7766-3775 The expanding geographic range of Phyllachora maydis, the fungus that induces Tar Spot infection on corn foliage, is increasingly threatening a Michigan industry that contributes over $1 billion to the state's economy annually. Foliar infection of maize by P. maydis is often difficult to detect early. Visible lesions initially appear tiny, ambiguous, and sparse, making them difficult to identify with the naked eye. Both farmers and breeders of corn desperately need better tools that allow early, definitive detection of lesions and provide more time for management decisions. This tool must verify presence of P. maydis and quantify infection severity as quickly as possible to allow growers the most options for treatment. Advances in machine learning now enable quantification of crop infection presence and severity using powerful object detection packages. With the growing availability of open-source tools, such as the Mask Region-Based Convolutional Neural Network (Mask R-CNN) and PlantCV, the field of plant disease phenotyping has more options for methods than ever before. I propose comparing the accuracy of two potential pipelines to quantify tar spot infection severity: one based on heuristic methods, involving techniques such as dynamic image colorspace thresholding, and the other based on the use of annotations, such as object detection and contour analysis. Comparison of these two methods will provide insight into challenges involved with phenotyping in the field as well as phenotyping foliar diseases using automated methods.

X-ray tomography (XRT) is a powerful and versatile tool for generating detailed non-destructive three-dimensional (3D) image data of large and complicated structures. In particular, excavated, cleaned and dried maize root crowns can be rapidly scanned, and the resulting 3D volumes processed in a computational feature extraction pipeline to provide a wide range of root trait measurements. These measurements provide rich data that give insights into how roots occupy 3D space in ways not possible with any 2D imaging/measurement systems. Hundreds of root crowns can be scanned in a moderate-throughput system, and multivariate statistical analyses can provide valuable insight into the role that genes and quantitative trait loci play in selected root traits. Research presented will describe XRT scan parameter optimization and its impact on root trait data generated by the feature extraction pipeline.

Imaging of plants using multi-camera arrays in high-density growth environments is a strategy for affordable high-throughput phenotyping. In multi-camera systems, simultaneous imaging of hundreds to thousands of plants eliminates the time delay in measurements between plants seen in plant-to-camera or camera-to-plant systems, which allows for the analysis of plant growth, development, and environmental responses at a high temporal resolution. On the other hand, high plant density, camera-to-camera variation, and other trade-offs increase the complexity of data analysis. Here we present two recent updates to the PlantCV image analysis package to improve usability when working with multi-plant datasets. First, we introduce a method to automate detection of plants organized in a grid layout, reducing the need to make separate workflows for each camera in a multi-camera system. Second, we reduced the number of input and output parameters for functions handling the shape and location of plants and introduce automatic iteration over multiple objects of interest (e.g. plants), reducing the level of programming needed to build workflows.

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.

Proper concentrations of several nutrients, such as iron (Fe), zinc (Zn) and potassium (K) in the soil are needed for plant growth. Thus, farmers sometimes test soils to determine fertiliser application rates. However, measuring soil and plant nutrient concentrations is relatively time-consuming and expensive. Therefore, usually, only a few samples are collected and analysed. This limits the scale of research but also poses some limitations on farming practices, as farmers cannot sample fields at high density in order to customize application rates. Cheap, fast, and high spatial resolution methods to measure soil nutrient concentrations would alleviate some of these limitations. This work aimed to determine the potential of hyperspectral imaging (HI) to predict the concentration of some soil nutrients. Soil samples were scanned by visible and near-infrared imaging systems with a total wavelength range of 450-1700 nm. Fe, Zn, and K were analyzed. Partial least-square regression models (PLSR) were used to correlate the relative reflectance of total Fe, Zn and K in the soil samples. The PLSR models could highly predict Fe concentration (R2 0.81, RMSE% 16.6) and performed moderately well for Zn (R2 0.30, RMSE% 0.9) and K (R2 0.47, RMSE% 5.58) concentrations. The overall results indicated that the hyperspectral technique coupled with PLSR could be an accurate and reliable method for determining soil nutrient concentrations.

Community colleges and minority-serving institutions are often ill-equipped with research facilities necessary for providing post-secondary students with opportunities for engagement in authentic research that would equip them with practical skills. Partnerships with research organizations can address gaps in professional training of post-secondary students to better equip them with skills aligned with industry needs, such as data handling. Through collaboration between the Donald Danforth Plant Science Center (DDPSC) and Harris-Stowe State University (HSSU), a local Historically Black College and University, a plant data science Course-based Undergraduate Research Experience (CURE) was developed to improve undergraduate access to research experiences and equip racially minoritized students with cutting-edge data science techniques. Biology and mathematics majors at HSSU used DDPSC researcher-generated plant image data to immerse themselves in real-world plant biology research. Students enrolled in this course used PlantCV, a Python-based image data processing software, to analyze the phenotypes of plants subjected to abiotic stresses. They interacted with image data to investigate heat stress responses in Zea mays (maize) and were exposed to complex interdisciplinary concepts that challenged their understanding of how biology and data science intersect. Students learned to analyze image data to extract conclusions based upon student-generated questions. Qualitative data from students' weekly surveys reveal the building of data science knowledge within students engaged in the CURE, providing insights for educators involved in planning and assessing undergraduate learning experiences at minority-serving institutions. This course

Image based high throughput plant phenotyping is a powerful tool to capture and quantify diverse plant traits. The available commercial platforms are often cost-prohibitive. This study describes the development of a low cost, automated plant phenotyping platform, which can acquire images, transfer data, segment the images, extract the traits and perform data analysis using low-cost microcomputers, cameras and IoT irrigation system. Quantifiable plant traits (e.g., shape, area, height, color) were extracted from the plant images using an in-house pipeline developed in R language. An experiment of water stress (waterlogging and drought) on Mentha arvensis (Menthol mint) crop (cv. CIM-Kosi) was conducted to demonstrate image traits being used as a proxy for plant response to water stress. It was found that the effect of drought stress on plant height and number of secondary branches could be correlated to color traits of plant canopy images. Also, the effect of waterlogging stress on chlorophyll and flavonoid content could be related to the shape traits of plant canopy images and effect on waterlogging on plant height and canopy width could be associated with color and texture traits. The imaging platforms could successfully demonstrate a viable low-cost solution for incorporating high-throughput plant phenotyping in various plant stress related research applications.

Branching patterns in plant roots are associated with complex traits such as stress-tolerance, yield, and the ability for carbon sequestration. The capability of the root system to branch allows the plant to search the soil for water and nutrients. For example, a reduction of higher order roots may determine how well a crop plant tolerates drought, whereas the ability to develop more higher order roots determines how well a crop plant tolerates a nutrient deficient soil. Measurements of traits such as rooting depth, root width or specific root length, however, often fail to capture the complex morphological arrangement of the root system. Therefore, a more rigorous analysis of root branching patterns is highly relevant as they are linked to the ability of plants to respond to abiotic stresses, such as drought and nutrient deficiency. Despite the need, it remains a challenge to extract information about branching patterns due to intersecting and overlapping roots in 2D and 3D imaging data. Such occlusion problems add ambiguity and outliers to root trait measurements. We present an algorithm to resolve such intersections in a globally optimal way based on simple heuristics such as straightness of roots - thus being dimension independent. This will enable quantitative analysis of how root branching patterns change in response to abiotic stress using shape descriptors. The possibility to computationally measure very dense branching structures with thousands of intersections will support the breeding of plants that withstand increasing areas of drought and nutrient deficiencies in the world.

To study how plants respond to their environment researchers use imaging phenotyping technologies. The use of image-based phenotyping has enabled researchers to analyse plants and produce data at a large scale. However, this large influx of data has created a 'big data' problem to emerge causing researchers to search for new innovative ways to tackle the challenges of processing their data in a reasonable timeframe. To address such issues, deep learning and data science techniques are being used to perform a comprehensive analysis. Here we use a Plant Screen™ compact system to image a series of barley plants using two different imaging sensors. This compact system contains an RGB top and side view camera and a hyperspectral visible near infrared (VNIR) camera. To streamline the processing and analysis of RGB and hyperspectral imaging, we are building a pipeline using a lightweight implementation of the U-Net architecture to improve the accuracy of semantic segmentation based on the raw images captured via the compact system. Several models were designed and developed, each of which was tailored to either the type of imaging sensor being used or the angle for which the images been provided were taken (e.g., top-down, side-view). Results showed that each model regardless of sensor or perspective produced an accuracy greater than 90% and could accurately segment cereal crops regardless of their size, shape or colour. These results demonstrate the feasibility of using DL models to semantically segment cereal crops imaged using either RGB or hyperspectral imaging sensors.

ORCiD: https://orcid.org/0000-0002-0550-7682 Keywords: cover crops, root system architecture, ecosystem services, root traits, gel imaging system. Cover crops are an emerging solution to the negative impacts of conventional agricultural practices. Through their essential ecosystem functions, cover crops can improve soil health and biodiversity during fallow periods in conventional crop rotation systems. Hairy vetch (Vicia villosa), winter barley (Hordeum vulgare), and purple top turnip (Brassica campestris) are cover crops that provide a variety of ecosystem services such as nitrogen fixation, nutrient capture, and soil remediation. Using a 4D gel imaging system, we were able to evaluate 3D root system architecture over time of these three cover crops in order to further understand root growth and development. The collected traits allowed us to compare root growth and RSA across the plant species and better understand how certain root traits are linked in ecosystem functions. The long, fibrous root system found in winter barley allows the plant to effectively catch nutrients and water in the soil. The large taproot and secondary roots found in turnip are able to break up compacted soil while maintaining a network of finer roots to scavenge for nutrients. Similar to purple top turnip, the taproot in hairy vetch may provide soil remediation, but the deeper roots in vetch allow for the plant to provide increased acquisition and fixation of atmospheric nitrogen.

Crop breeding relies on the numbers game. The higher the number of locations and entries evaluated, the higher the probability of developing superior cultivars. One major challenge facing breeders of perennial cool season forage crops is the number of biomass harvests per season. In regions with mild winters, alfalfa is harvested every four weeks, six to seven times a year. This creates an operational bottleneck limiting the number of entries and testing locations. Substantial investments were made in the development of automated solutions for precision Ag for row crops. The adoption of these platforms to forage crops rests on their accuracy in estimating biomass yield and cost-effectiveness. This work focused on evaluating the sensitivity of popular unmanned aerial vehicles (UAVs) and imaging strategies for optimal real-time biomass estimates in perennial forage crops. Experimental plots consisting of single plants, row plots, and sward plots were used for a hybrid data collection approach including direct measurements and remote sensing. UAV platforms equipped with a 42-megapixel RGB camera (Sony Alpha 7Rii), a five-band multispectral system (MicaSense RedEdge MX), a hyperspectral sensor (Resonon-Pika L), and a LiDAR (LiDARUSA Revolution 120) were tested. Images were used to generate 3D canopy models of vegetation in the field and to compute morphometric and spectral indices descriptive of vegetation coverage, health and vigor. Harvested biomass yield was used to validate the values derived from UAVs. Preliminary results suggest that the simple red-green vegetation index may be sufficient to give a reliable estimate of biomass yield.

The research introduces a novel algorithm called HyperStressPropagateNet that uses deep neural network based time series modeling to illustrate the qualitative and quantitative propagation of drought stress in a plant using hyperspectral imagery. The hyperspectral cameras typically capture a broad range of wavelengths at very narrow intervals of a few nanometers creating a hyperspectral cube. HyperStressPropagateNet uses spectral band difference-based segmentation method to create the binary mask of the plant which is then used to segment the plant in all bands of a hyperspectral cube to create the reflectance spectra at each plant pixel. The algorithm uses convolutional neural networks to classify the reflectance spectra generated at each pixel into either stressed or unstressed categories to determine the temporal propagation of stress. The limited water availability in the soil is confirmed by changes in the soil water content (SWC) measured using a hand-held device. The excellent correlation between the SWC and the corresponding temporal progression of percentage of stress pixels computed by HyperStressPropagateNet demonstrates the efficacy of the method. The algorithm is evaluated on a dataset of image sequences of cotton plants captured by the hyperspectral camera in the LemnaTec Scanalyzer 3D High Throughput Plant Phenotyping Platform in the University of Nebraska-Lincoln, USA. The excellent performance of the method is established based on evaluations using various metrics, e.g., confusion matrix, precision-recall curve, and F1-score. The method has the potential to be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture.

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.

BodyText: Hyperspectral imaging (HSI) system can facilitate the study of crop physiological responses to abiotic stress. It has been established in automated controlled-environment across the globe. Nonetheless, each crop in every new environment requires specific experimental design and data analysis pipeline. At Purdue University's Ag Alumni Phenotyping Facility (AAPF), 15 indica and eight tropical japonica rice genotypes were raised up to 13 weeks old under two nitrogen treatments. HSI data were collected two to three times per week and 14 physiological traits relating to growth, photosynthesis capacity and water transportation were measured manually. With principal component analysis (PCA), physiological trait data showed the effects of subpopulation and treatment whereas only treatment effect could be revealed in HSI data. Changes of reflectance around 715 nm (in the red edge region) were associated with the treatment effect in HSI data based on the loadings of PCA. By training support vector machine classifiers, we found that classification accuracy of treatment levels in HSI data was 80% or greater when the rice plants were six to 10 weeks old. Furthermore, leaf-level nitrogen content (N, %) and carbon to nitrogen ratio (C:N) could be predicted from HSI data by building partial least squares regression models (PLSR) with featured wavelengths. The í µí± ! values for N and C:N were 0.83 and 0.73, respectively, and normalized root mean square error of prediction for N and C:N were 13.67% and 14.39%, respectively (in validation datasets). This is the first study that showed the potential use of HSI on rice at AAPF.