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: [ORCiD of presenting author] Jaebum Park [0000-0001-6459-909X] AND/OR Max Feldman [0000-0002-5415-4326]Tuber size and shape, colorimetric characteristics of tuber skin and flesh, and tuber defect susceptibility are all factors that influence the adoption of potato cultivars. Despite the importance of these characteristics, our understanding of their inheritance is limited by our inability to precisely measure these features on the scale needed to evaluate breeding populations. To alleviate this bottleneck, we have developed a low-cost, semi-automated workflow to capture data and quantify each of these characteristics using machine vision. This workflow was applied to assess the phenotypic variation present within 189 F1 progeny of the A08241 breeding population and map the genetic basis of tuber characteristics. Several medium-to-large effect, quantitative trait loci (QTL) were found to be associated with different measurements of tuber shape. These results indicate that quantitative measurements acquired using machine vision methods are reliable, heritable, and can be used to map and select upon multiple traits simultaneously in structured potato breeding populations.

ORCiD: [https://orcid.org/0000-0003-0655-2343] Increasing weather variability is affecting the overall productivity of agriculture. In this scenario, current crop improvement science is essential to improve productivity while retaining the quality of plant products. There has long been an interest in using process-based modeling to examine the interaction between environment and genotype. The methodological challenges to better predict how various environmental conditions may impact novel genotypes and it has been a fundamental barrier to model parameterization. Thus, a phenotypic campaign was conducted to collect a comprehensive physiological dataset from a panel of 25 genotypes (including both breeder panel and diversity panel) in the summer of 2022. Additionally, an unmanned aerial vehicle (UAV) was used to gather remote sensing data. The red-green-blue (RGB) 3D point cloud, NDVI (Normalize difference vegetation Indices), and LIDAR (Light detection and ranging) were also used to identify the trait variations among the genotypes. The data is being analyzed to explain the physiological and phenotypic trait differences. The outcome of this project would help to develop a genetically informed, realistic soybean model. Finally, it will help breeders and growers in locating high-yielding cultivars for the appropriate geographical areas.

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.

High night air temperature stress (HNT) challenges rice production. Findings indicate 10% yield reduction for every 1 o C of increase in night air temperature. The responses of rice to HNT stress have been analyzed in limited number of genotypes mostly under greenhouse conditions. One of the limits for these studies under field conditions is implementing HNT stress on critical rice growth stage. The physiological and metabolic responses of rice to HNT stress under field conditions are not fully understood, thus, field studies are needed. Field-based phenotyping infrastructure that can house rice germplasm and stress imposition using computer-based system basing on ambient temperature still do not exist. In this study, six high tunnel greenhouses were built in a field experimental station in Harrisburg, AR in a split-plot design. These movable infrastructures fitted 310 rice accessions from the Rice Diversity Panel 1 (RDP1) and 10 hybrids from RiceTec. Each high tunnel greenhouse had heating and a cyber-physical system that recorded ambient air temperature and increased night air temperature relative to ambient temperature at the flowering stage. The system successfully imposed HNT stress of 4.01 o C and 3.94 o C as recorded by Raspberry Pi sensors for two weeks in the 2019 and 2020 cropping seasons, respectively. These greenhouses were able to endure constant flooding and resist heavy rain and 40-50 miles/h winds. Grain quality and other biochemical assays are still ongoing to fully assess the effects of HNT in the rice accessions and the hybrids.

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.

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.

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.

The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.

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

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.

Joelle Claussen1, Antje Wolff2, Markus Rehak1, Johannes Wolff2, Bas van Eerdt3, Stefan Gerth1

Reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant varieties. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Machine learning (ML) methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)]. This study uses disease severity from both visual field ratings and ML-based (using images) severity ratings collected from 473 accessions. Images were processed through an ML framework that identified soybean leaflets with SDS symptoms, and then disease severity was quantified on those leaflets into few classes. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS, such as ss715584164 and ss715610404, or near the potentially novel candidate genes, such as ss715583703 and ss715615734. Within previously reported SDS quantitative trait loci there were significant SNPs from both visual rating and image-based ratings. The results of this study provide an exciting avenue for using ML to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field stress phenotyping.

Hemp (Cannabis sativa L. <0.3% THC) is a versatile crop cultivated for grain, fiber, and cannabinoids used for health and wellness purposes. Following the passage of the 2018 Farm Bill, which removed hemp from the controlled substances list, there is potential for expanded hemp acreage and a concomitant need to breed cultivars with suitable agronomic performance in US growing regions. Understanding the relationships between morphological, biochemical, and spectral traits with respect to yield will allow for high-throughput phenotyping and advanced breeding efforts. For five years, the Cornell Hemp research team has evaluated high-cannabinoid hemp cultivars in replicated field trials, with populations originating from both commercial sources and the Cornell Hemp Breeding Program. These field trials evaluated plants for phenotypic traits, including plant height, morphology, flowering time, cannabinoid concentration, and total biomass yield. We have also determined key morphometric measurements that are correlated with end-of-season biomass yield. Also, both floral and foliar cannabinoid samples were correlated with end-of-season whole plant biomass cannabinoid concentration. To improve phenotyping efficiency, an unmanned aerial vehicle (UAV)-based multispectral system was deployed to characterize morphological and biochemical traits over time. These datasets are being used to develop high-throughput phenotyping methods to predict biomass yield, and in the future cannabinoid concentration and flowering time.

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.

ORCiD: https://orcid.org/0000-0003-4699-3733 Rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhancing scion physiology and attributes. Therefore, understanding how the rootstock affects photosynthesis is insightful for genetic improvement of either genotype in the grafted grapevines. Photosynthetic parameters such as maximum rate of carboxylation of RuBP (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax) has been identified as ideal targets for breeding and genetic studies. However, techniques used to measure these photosynthetic parameters are time consuming and subjective to leaf level which is complex to implement at field scale. Hyperspectral remote sensing uses the optical properties of the entire vine to predict photosynthetic capacity at canopy level. In this study, estimates of Vcmax and Jmax were assessed using different machine learning models: PLS (Partial least Squares), LR (Least Angle Regression), LASSO (Least Absolute Shrinkage and Selection Operator), PCR (Principle Component Regression) based on leaf reflectance metrics obtained with hyperspectral wavelength ranging from 400 to 1000nm. Prediction models were developed for six different rootstock genotypes with common scion Marquette considering three different sampling dates carried out in Brookings, South Dakota in 2021. Preliminary results indicate that each rootstock has distinctly different Vcmax and Jmax profiles across the season. From the model assessment, PLS was found to have robust prediction of Vcmax with R2 of 0.53 and for Jmax with R2 of 0.63. Multiple year trials will be used to validate precise and rapid quantification of photosynthesis using hyperspectral remote sensing.

Interactive Annotation for object delineation can be considered as a semi-supervised few-shot learning problem where machine learning models learn from a small set of annotated pixels and generalize to the entire picture to extract the object of interest. One aim of interactive annotation is to reduce the effort of manually labeling data. Some existing works attempted to address this problem with deep metric learning so that the encoding layers in the network are able to extract features that boost discriminability among pixels belonging to different classes. To keep the data structure in the embedding space, metric loss with prototypes has been proposed. In our work, we improved the existing methods by developing a new objective function to update the network and prototypes simultaneously. The prototypes are optimized based on the loss that enhances their dissimilarity instead of clustering or sampling from the dataset. Moreover, we designed a GUI with the proposed method for interdisciplinary collaboration of image-support plant phenotyping studies.

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.