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.
The growing world population increases demand for agricultural production, which is becoming even more challenging as climate change increases global temperatures and causes more extreme weather events. Using high-throughput phenotyping, this study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of both stresses. Heat alone causes the largest amounts of tissue damage and the combination of heat and drought causes the largest decrease in plant biomass compared to other treatments, however, we identified heat alone as being the most detrimental stress condition. Notably, we identified Bd21-0, the reference line for B. distachyon, as not having robust growth under stress conditions, especially in the heat-drought combined treatment. We found climate of origin (climate data from the accessions' collection locations) to be significantly associated with height and percent of plant tissue damage under the conditions assessed, indicating a relationship between climate of origin and B. distachyon phenotype under drought and heat stresses. Additionally, genome wide association mapping found a number of genetic loci associated with changes in plant height, biomass, and the amount of damaged tissue under stress. Significant SNPs were closely located to genes known to be involved in plant responses to abiotic stresses. The anticipated increase in drought and heat stress as a result of climate change and the distinct impact of stresses in combination, as demonstrated in this study, underscores the importance of phenotyping plants under multiple stresses that frequently converge.
High-oil tobacco varieties have been recently engineered to produce increased leaf oil content for future food and fuel needs. An engineered variety of Nicotiana tabacum produces ~30 percent of leaf dry weight in lipids in the form of triacylglycerol (TAG), a significant increase relative to the less than 1 percent storage oil normally found in wild-type leaves. This high-oil tobacco also accumulates oil bodies in stomatal guard cells. In order to understand the impact of oil on guard cell shape, aperture, and dynamics, we have co-opted computer vision tools in PlantCV to create an accurate, flexible, and high-throughput method for microscopy image analysis of stomata. To this end, leaf impressions are made with silicone putty; clear nail polish peels of the putty impressions are imaged using light microscopy. Binary thresholding followed by point-and-click regions of interest and morphology calculations provide stomatal counts, aperture, and other shape characteristics. Applying this method to high-oil tobacco demonstrated reduced stomatal aperture but the same number of stomata per unit leaf area, providing a mechanistic explanation of high-oil tobacco responses to high temperature and water deficit stresses.
3D scans of real world objects are often represented by point clouds, creating XYZ-coordinates of individual scan points. However, unlike point clouds that are generated from CAD data, points generated from a real world scene lack information about their local context, making segmentation of the structural information contained in the data difficult. Using neural networks (e.g. PointNet) has shown promising results. However, this approach is not well suited for scans of large areas of similar objects, like e.g. a wheat field, because of limitations of the input vector size of the neural network. In addition, point clouds are often unordered, further complicating processing. Since point clouds of biological objects often contain recurring features, we propose to subdivide the point cloud into locally neighboring subsets with a fixed number of points. The collection of subsets can then be used to train neural networks. This approach preserves the original resolution of the point cloud while offering simple data augmentation concepts like creating a number of different subset collections from the same ground truth. There are several advantages to this approach, like significantly simplifying the training phase, because a single, large annotated scan can be sufficient for training, utilizing the similarity of the instances of a plant in the field.
Malting is the controlled germination of a cereal grain. In barley, malted grains provide the fermentable sugars necessary for brewing and distilling processes. Harvested barley must adhere to strict industry quality standards to be considered for malting including robust hydrolytic enzyme content, low protein content and high rates of germination. Failure to meet quality metrics results in significant loss of market value and presents a risk to growers considering malting barley. Modern cultivars of malting barley are susceptible to preharvest sprout (PHS), or germination of the seed prior to harvest, resulting in premature endosperm modification, reduced enzyme content and poor malthouse germination. Seeds with PHS damage fail quality assessments and are sold for feed at reduced prices. Most presprouted grain shows no visual signs of damage and accepted methods to assess PHS damage including Hagberg falling number and stirring number (Rapid Visco Analyzer), which are costly and destructive to the seed. To address this need, we applied time-series hyperspectral imaging of barley seeds with varying levels of PHS damage and used a deep neural network to predict stirring number and alpha amylase values, which are indicators of sprouting. Prediction models were generated for each of the seven genotypes tested individually and also for all genotypes when combined. Our prediction models had mean average errors from 10.5 to 23.9 and root mean square errors from 19.0 to 35.1 demonstrating the applicability of hyperspectral imaging as a high-throughput, nondestructive method for predicting levels PHS damage in malting barley.
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.
Temporal analysis reveals diverse root system architecture and development differences among pennycress accessions to nitrate nutrition (Thlaspi arvense L.) Roots have a central role in plant resource capture and are the interface between the plant and the soil affecting multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich oil seeds amenable as a biofuel (30-35% oil) or high-protein animal feed. The objective of this research was to (1) precisely characterise root system architecture and development, (2) understand adaptive responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity. Using a root imaging and analysis pipeline 4D pennycress root system architecture was characterised under four nitrate regimes (from zero to 5 mM nitrate concentration) across four time points (days 5, 9, 13 and 17 after sowing). Significant nitrate condition response and genotype interaction was identified for many root traits with a greater impact on lateral root traits. In trace nitrate conditions a greater lateral root count, length, interbranch density, and a steeper lateral root angle was observed compared to high nitrate conditions. Genotype by nitrate condition interaction were observed for root width, width depth ratio, mean lateral root length, and lateral root density. These results illustrate root trait variance available in pennycress accessions and useful targets for breeding of improved nitrate responsive cover crops for greater productivity, resilience, and ecosystem service.
High throughput phenotyping and quantitative genetics have enabled researchers to identify genetic regions, or markers, associated with changes in phenotype. However, going from GWAS markers to candidate genes is still challenging. When selecting candidate genes for ionomic GWAS markers, we curated a collection of well-known ionomic genes (KIG) experimentally shown to alter plant elemental uptake and their orthologs in 10 crop species: 2066 genes total. Yet when compared to ionomic GWAS markers, over 90% of significant markers were not linked to a KIG - indicating the list is incomplete and many causal genes are unknown. Continuing to use only functional annotations as candidate selection criteria will keep efforts biased toward well-known genes and hinder the characterization of unknown genes. We propose an unbiased computational approach that compares analogous GWAS markers from multiple species and searches for conserved genes linked to trait markers. Like the KIG list, we expect many of these unknown candidate genes to have orthologs in other species. By leveraging the evolutionary relationship of these conserved genes, rather than prior knowledge and gene annotations, this method: 1) finds more candidate genes than we expect from random chance, 2) selects and prioritizes candidates in poorly annotated species, and 3) includes unknown genes in the results. With this approach, we now have an unbiased list of gene candidates across 19 ionomic traits in model species and crop species to verify in future experiments.