Jinjeong Lee, Su Jung Ra, Sung Mi Kim, Eunhee Kim, Myeong-Jin Kang,

Resource allocation drives the above-ground distribution of mass in grass plants across discrete developmental units called phytomers. Although the number of phytomers varies in genetically-identical grasses, there frequently isn't an associated variance in some summary phenotypes. To understand what may be driving this, we tracked the growth of 30 S. italica plants from genotypes B100 and A10.1. We experimentally observed that plants from the genotype B100 had between 20 and 22 phytomers, while plants from the genotype A10.1 had between 7 and 9 phytomers. B100 plants with more phytomers (e.g., 22) did not grow taller or have more total leaf length, despite having more leaves than plants with fewer phytomers (e.g., 20). A10.1 plants with more phytomers (e.g., 9) did grow taller and had more total leaf length than those with fewer phytomers (e.g., 7). We developed a dynamical model to determine if these patterns are emergent from the underlying growth structure. The model is parameterized using the number of phytomers and related developmental time parameters: leaf emergence, stem and leaf elongation time, panicle emergence, and flowering time. The model uses the semi-sequential nature of phytomer growth as its structure. The model predicts that differences in timing of the shift to reproductive growth could explain the patterns observed. Experimental measurements suggest this shift is primarily due to tuning the developmental time parameter controlling the units contributing to the stem, rather than leaves.

Object detection algorithms have heavily relied on deep learning techniques to estimate the count of grapes as the resulting quality of grapes is directly correlated to its yield. With temporal analytics, early actions and logistical organizing can be performed to maintain the quality of grapes. However, the issue with using these object detection algorithms is that they are limited to counting only the visible grapes, thus omitting hidden grapes, and in turn affecting the true estimate of grape yield. Many grapes are occluded because of either the compactness of the bunch cluster, or due to canopy interference. Therefore, models need to be able to estimate the unseen berries in order to give a near true yield estimate. An end-to-end framework is proposed in which the grape clusters are first segmented using a deep learning model, after which the extracted candidate regions of grape clusters are fed to a CNN regression model that can estimate the count of berries by incorporating a correction factor. A new dataset is also proposed which consists imagery of grape clusters, along with their ground truth values of grape count and weight. The proposed framework will also be tested using three open-source datasets and will encourage future research in determining which features of grapes can be leveraged to correct grape counting algorithms and produce higher accurate results.

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

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.

Hyperspectral imaging is a non-destructive imaging technique used in plant phenotyping to collect and analyze an array of electromagnetic information in visible (380-700 nm) and near-infrared wavelengths region (700-2,500 nm). Hyperspectral imaging can provide information of plant responses under various biotic and abiotic stress, e.g., drought, temperature rising, disease, and nutrition deficiency. We present a hyperspectral data processing pipeline designed for the data collected at Ag Alumni Seed Phenotyping Facility (AAPF) in Purdue University, USA. The procedure consists of initializing a processing session, radiometric calibration with white and dark references, geometric calibration (registration) of visible and near infrared (VNIR) and shortwave infrared (SWIR) images, vegetation and non-vegetation classification, vegetation indices calculation of a plant area, exporting data products, and quality control. In concern of large data size of hyperspectral data, we highlight the need to save memory usage during computation and save disk space for data products. We also address the need of human interpretable images in the hyperspectral data products for plant scientists without experiences in hyperspectral imaging. We expect the developed procedure could improve robustness of large hyperspectral data processing and promote the usage of hyperspectral data by increasing interpretability.

Source-Sink Regulated Senescence in Commercial Maize Germplasm

Minirhizotron technique (MR), an image-based root phenotyping technology, has expanded our understanding of in situ root responses to changing environmental conditions. However, the conventional manual approaches to capturing and analyzing images are time-consuming, thus constraining the size and frequency of sampling and interpretation. To address this bottleneck, an automated MR system was developed, which worked in a fully automatic manner according to the pre-set schedule. Users can remotely control the system and download images. This system was tested in a net house by imaging bell pepper roots on a daily basis, which shows superior performance over commercial manual MR systems in terms of image resolution and sampling frequency. Besides, an image analysis model was built on convolutional neural networks to estimate root length from MR images directly without segmentation in the training process. This model was trained on a dataset of ~18,000 tomato root images taken by a manual MR camera and was used for estimating root length on 832 bell pepper root images taken by the automated MR camera. The high correlation coefficient (R 2 = 0.854) between the model estimation and manual measurement proved that this model generalizes well over different crop roots and camera types. Therefore, high-frequency in situ root phenotyping can be achieved by the automated Minirhizotron image acquisition and analysis tools proposed here.

A document by Jose C. Tovar. Click on the document to view its contents.

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

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.

A document by Mao Li. Click on the document to view its contents.

Yeongtae Kim, Nyunhee Kim, Eunsook An, Younguk kim, JaeYoung Kim, Chaewon Lee, JeongHo Baek, HwangWeon Jeong, Hyenso Ji, Song Lim Kim

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.

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.

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.

Global wheat production needs to increase by 60% to ensure food security in the future. Radiation use efficiency (RUE), defined as dry matter production per unit of light energy consumption, is an important trait that contributes to wheat yield potential. Traditionally, RUE is estimated through sequential biomass cuts evaluated against cumulative light interception, which is less precise and non-specific to genotypes. 3D models have recently been shown promise in estimating light interception when used along with ray tracing algorithms, mostly deployed in single plant-based models, while light interception at the canopy level remains to be explored. In this study, a mobile robotic phenotyping platform equipped with dual multispectral laser sensors was used to generate canopy 3D data. Using this platform, 100 spring wheat genotypes were scanned at heading stage to understand the genetic variation for RUE and its associated traits under field conditions. Ray-tracing algorithms were used to estimate the fraction of intercepted photosynthetically active radiation (FIPAR) for all genotypes, validated through a hand-held light ceptometer. Genotype-specific RUE was calculated as a slope between dry biomass and accumulated PAR. 3D model-based FIPAR was in close agreement with ceptometer-derived FIPAR. 3D model-derived RUE showed a large genetic variation across 100 wheat genotypes. It explained a higher variation in grain yield than ceptometer-derived RUE. These results indicate that canopy 3D models can be used as a rapid method for estimating canopy RUE in wheat, and potentially are extendable to other cereals.

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.