High-throughput Phenotyping Data Research Articles

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine.

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.

Diverse signatures of convergent evolution in cactus-associated yeasts.

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning-based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall-degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle-and perhaps more generally lifestyles favoring thermotolerance-might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity.

QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data

Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding. We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds. Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R2 of 0.67 versus 0.56 and an RMSE of 0.12 m vs. 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F1DH and F2DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

Wheat Teacher: A One-Stage Anchor-Based Semi-Supervised Wheat Head Detector Utilizing Pseudo-Labeling and Consistency Regularization Methods

Wheat breeding heavily relies on the observation of various traits during the wheat growth process. Among all traits, wheat head density stands out as a particularly crucial characteristic. Despite the realization of high-throughput phenotypic data collection for wheat, the development of efficient and robust models for extracting traits from raw data remains a significant challenge. Numerous fully supervised target detection algorithms have been employed to address the wheat head detection problem. However, constrained by the exorbitant cost of dataset creation, especially the manual annotation cost, fully supervised target detection algorithms struggle to unleash their full potential. Semi-supervised training methods can leverage unlabeled data to enhance model performance, addressing the issue of insufficient labeled data. This paper introduces a one-stage anchor-based semi-supervised wheat head detector, named “Wheat Teacher”, which combines two semi-supervised methods, pseudo-labeling, and consistency regularization. Furthermore, two novel dynamic threshold components, Pseudo-label Dynamic Allocator and Loss Dynamic Threshold, are designed specifically for wheat head detection scenarios to allocate pseudo-labels and filter losses. We conducted detailed experiments on the largest wheat head public dataset, GWHD2021. Compared with various types of detectors, Wheat Teacher achieved a mAP0.5 of 92.8% with only 20% labeled data. This result surpassed the test outcomes of two fully supervised object detection models trained with 100% labeled data, and the difference with the other two fully supervised models trained with 100% labeled data was within 1%. Moreover, Wheat Teacher exhibits improvements of 2.1%, 3.6%, 5.1%, 37.7%, and 25.8% in mAP0.5 under different labeled data usage ratios of 20%, 10%, 5%, 2%, and 1%, respectively, validating the effectiveness of our semi-supervised approach. These experiments demonstrate the significant potential of Wheat Teacher in wheat head detection.

A corn canopy organs detection method based on improved DBi-YOLOv8 network

Corn canopy organs detection is critical in obtaining high-throughput phenotypic data. Accurate identification of each organ can provide a reliable data source for canopy phenotype determination, which has significant theoretical and practical value for corn variety breeding, cultivation management, and high-quality and high-yielding production. Due to the difficulty in quickly identifying corn canopy organs in the natural environment of the field, it is challenging to obtain high-throughput phenotypic data. Therefore, this paper proposed a method for corn canopy organs detection based on an improved network model (DBi-YOLOv8). Firstly, the Raspberry Pi 4B was used as the sensor control center to construct an embedded system for corn canopy image acquisition and collected 987 images of corn plants. Secondly, the improved deformable convolution and Bi-level routing attention were embedded into the backbone and neck structures of the YOLOv8 network. With training the improved network, a corn canopy detection model was obtained, which enabled the rapid detection of corn canopy organs. Finally, the LTNS algorithm and TBC algorithm were proposed for counting of the number of leaves, ears, and tassels. On the testing set data, the detection performance of the model was analyzed through different evaluation metrics. The results showed that the mAP and FPS of the detection model were 89.4% and 65.3, which increased by 12% and 0.6 compared to the original model. In addition, both algorithms have high reliability, with the coefficient of determination R2 for counting crown leaves, ears, and tassel branches being 0.9336, 0.8149, and 0.917, respectively. This achievement proposed an accurate, non-destructive, and fast corn canopy organs detection model, providing reliable technical support for quantifying various traits of corn plants, field crop growth monitoring, and elite variety breeding.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs.

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Study of Phenotype Analysis of Maize Plant: Hyperspectral Image Perspective

Abstract: Maize, alongside rice and wheat, constitutes a trio of crops responsible for over 50% of the global calorie consumption. To address the rising food demand, enhancing the productivity and stress resilience of these crops becomes imperative. However, the progress of plant breeding initiatives is hindered by the cost and time constraints associated with acquiring plant phenotype data. To overcome these limitations and advance the field, there is a need for datasets that connect new forms of highthroughput phenotype data, gathered from plants, to the performance of identical genotypes in diverse agronomic settings and habitats. These datasets will pave the way for the development of innovative statistical and Machine Learning techniques, empowering researchers to expedite crop improvement efforts effectively.

Development of 3‐D point cloud imaging and 2‐D image processing methods for high‐throughput phenotyping of perennial ryegrass spikes

AbstractAdvances in technology have increased the adoption of high‐throughput phenotyping (HTP) methodologies, potentially replacing laborious and time‐consuming measurements and data recording. One promising HTP tool for fine‐featured and small‐sized characteristics is the three‐dimensional (3‐D) scanning and imaging system, but the utility of present two‐dimensional (2‐D) technology has not been fully explored for this purpose. The objective of this work was to reduce assessment and field evaluation time by developing 2‐D photogrammetric and 3‐D point cloud imaging methods for HTP of spike characteristics in perennial ryegrass (Lolium perenne L.), with special attention to traits that might be associated with seed retention. These HTP imaging systems were compared with direct data capture by hand on spikes of 21 diverse global accessions of perennial ryegrass. The Fiji (ImageJ) open‐source imaging software was used for photogrammetric analysis of spike structure, including spike length, spikelet number, internode length, and 2‐D curvature of the spike. An original approach to nondestructive HTP for crop characterization was developed using the commercially available Artec Space Spider. This 3‐D optical sensor was used to generate dense 3‐D point clouds to measure spike length, spikelet number, internode length, spikelet length, spikelet angle, and 3‐D curvature of the spike. Both methods were found to accurately characterize the subject; the 3‐D method was slower than 2‐D but was more precise (p ≤ 0.01) than 2‐D image analysis with a linear measurement deviation of only 0.17%. Fiji was effectively used for post‐processing image analysis, and the Space Spider can be used directly in the field to support HTP data collection. This non‐destructive field measurement system facilitates HTP in perennial ryegrass spikes and likely in other applications.

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions.

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

Development and deployment of a big data pipeline for field-based high-throughput cotton phenotyping data

In this study, we propose a big data pipeline for cotton bloom detection using a Lambda architecture, which enables real-time and batch processing of data. Our proposed approach leverages Azure resources such as Data Factory, Event Grids, Rest APIs, and Databricks. This work is the first to develop and demonstrate the implementation of such a pipeline for plant phenotyping through Azure's cloud computing service. The proposed pipeline consists of data preprocessing, object detection using a YOLOv5 neural network model trained through Azure AutoML, and visualization of object detection bounding boxes on output images. The trained model achieves a mean Average Precision (mAP) score of 0.96, demonstrating its high performance for cotton bloom classification. We evaluate our Lambda architecture pipeline using 9,000 images yielding an optimized runtime of 34 minutes. The results illustrate the scalability of the proposed pipeline as a solution for deep learning object detection, with the potential for further expansion through additional Azure processing cores. This work advances the scientific research field by providing a new method for cotton bloom detection on a large dataset and demonstrates the potential of utilizing cloud computing resources, specifically Azure, for efficient and accurate big data processing in precision agriculture.

Use of Unmanned Aerial System (UAS) Phenotyping to Predict Pod and Seed Yield in Organic Peanuts

Peanut (Arachis hypogaea L.) is a highly nutritious food that is an excellent source of protein and is associated with increased coronary health, lower risk of type-2 diabetes, lower risk of breast cancer and a healthy profile of inflammatory biomarkers. The domestic demand for organic peanuts has significantly increased, requiring new breeding efforts to develop peanut varieties adapted to the organic farming system. The use of unmanned aerial system (UAS) has gained scientific attention because of the ability to generate high-throughput phenotypic data. However, it has not been fully investigated for phenotyping agronomic traits of organic peanuts. Peanuts are beneficial for cardio system protection and are widely used. Within the U.S., peanuts are grown in 11 states on roughly 600,000 hectares and averaging 4500 kg/ha. This study’s objective was to test the accuracy of UAS data in the phenotyping pod and seed yield of organic peanuts. UAS data was collected from a field plot with 20 Spanish peanut breeding lines on July 07, 2021 and September 27, 2021. The study was a randomized complete block design (RCBD) with 3 blocks. Twenty-five vegetation indices (VIs) were calculated. The analysis of variance showed significant genotypic effects on all 25 vegetation indices for both flights (p r = 0.44) and seed yield (r = 0.64). These results can be used to further advance organic peanut breeding efforts with high-throughput data collection.

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.

PSX-A-8 Updating Pre-Existing Genetic Evaluations System to Evaluate High-Throughput Data in Purebred and Crossbred Beef Cattle

Abstract High-throughput technologies are available to aid producers in efficiently and sustainably raising many animals, including automated sensory technologies for phenotype measurement and genomic data. However, uptake of genetic selection by the beef cattle sector has been limited by segmentation of the industry. Therefore, easy to use genetic evaluations systems (GES) that can convert ‘big data’ into real-time and comprehensive results are needed, so that beef cattle producers can utilize the technologies that are becoming available to make accurate breeding decisions. The purpose of this study is to update a pre-existing purebred and crossbred beef cattle GES to become both flexible and efficient in its ability to evaluate high-throughput phenotypic data, and to assess the feasibility of including genotypes in a single-step genetic evaluation procedure (ssGBLUP). Firstly, computational operations required for the calculation of breeding values will be quantified and evaluated. A purebred and crossbred reference population will be assembled using data from Canadian beef breed associations purebred (n = 186,928) and commercial animals (n = 14,406). Multiple breeds will be considered (Angus, Charolais, Hereford, and Simmental), and multiple phenotypes analyzed (birth weight, weaning weight, yearling weight, and calving ease). Key component matrix operations with and without the use of Python Libraries (NumPy) will be evaluated. Computational performance of the different strategies will be compared, including CPU time, and memory allocation. Subsequently, a simulated ssGBLUP will be conducted using a population which mimics that observed in the preliminary analysis. Computational costs associated with the implementation of ssGBLUP will be compared to the existing GES. The results of this study will be directly applied to provide beef producers with the tools to improve the genetics of their own herds. This will facilitate the uptake of technology by the industry, thus increasing the economic value and sustainability of beef production.

A comparison of high-throughput imaging methods for quantifying plant growth traits and estimating above-ground biomass accumulation

Image-based estimation of above-ground biomass accumulation is recognized as the predominant asset of breeding programs for accelerating gains in crop adaptation and productivity. High-throughput phenotyping (HTP) has the potential to greatly facilitate genetic improvements by dissecting morphological traits which can serve as accurate predictors of optically sensed plant biomass. Thus, various high-throughput data acquisition methods have been recently developed to quantify desirable phenotypes from images. Novel insights are essential to provide helpful guidelines to breeders for the optimal selection of phenotyping approaches aimed at estimating plant biomass. In this study, three representative HTP data acquisition methods based on two-dimensional (2D) image analysis, Multi View Stereo (MVS)-Structure from Motion (SfM) three-dimensional (3D)-reconstruction and Structured Light (SL) 3D-scanning were compared for estimating fresh (FAGB) and dry above-ground biomass (DAGB) weight of potted plants at early growth stages. Two crop species with contrasting canopy shapes and architectures, namely maize (Zea mais L.) and tomato (Solanum lycopersicum L.), were used as model plants. First, the performances of each sensing approach were tested in the accurate reproduction of the major phenotypic traits and, secondly, in the reliable fresh/dry AGB estimation from the relevant allometric equations calibrated according multi- (six sampling dates, once a week) and mono-temporal (one sampling date at harvest time) datasets. The overall results demonstrated the effectiveness of the tested methods in reproducing the salient features of canopies with increasing architectural complexity, including plants’ height (R2̅ = 0.98, rRMSE̅ = 7.73 % and AIC̅ = 475.07), shoot area (R2̅ = 0.91, rRMSE̅ = 29.53 % and AIC̅ = 1369.77) and convex hull volume (R2̅ = 0.88, rRMSE̅ = 27.32 % and AIC̅ = 818.19). In this context, the shoot area associated with the age of the plant was found to be the most indicative phenotypic determinant for an accurate estimation of FAGB and DAGB. Accordingly, the greater ability of the 2D image analysis in quantifying canopies of elongated plants characterized by thin organs ensured the best estimates of fresh/dry biomass accumulation in maize (0.98 ≤ R2̅ ≤ 0.99 % and 8.98 % ≤ rRMSE̅ ≤ 16.03 %, considering multi- and mono-temporal calibrated models). Contrariwise, the MVS-SfM 3D-reconstruction of more complex canopies with compact habit was advantageous for the accurate prediction of above-ground DAGB and FAGB dynamics in tomato (R2̅ = 0.99 % and 6.70 % ≤ rRMSE̅ ≤ 15.82 %, considering multi- and mono-temporal calibrated models). These findings provide references to carefully select the best suited HTP data acquisition approach for the accurate estimation of biomass accumulation across plants of different canopy complexity, thereby paving the way to break through current phenotyping bottlenecks in breeding applications for current and future food security.

The field phenotyping platform's next darling: Dicotyledons.

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Bioenergy elephant grass genotype selection leveraged by spatial modeling of conventional and high-throughput phenotyping data

The burning of fossil fuels contributes to global warming. Using renewable energy sources such as elephant grass biomass mitigates anthropogenic impact on nature. The genetic selection of high-yield elephant grass genotypes is important to increase the use of this forage for energy generation. Unmanned aerial vehicles have been used for data collection and optimization of the selection of genotypes. However, statistical tests should be conducted to study the suitability of vegetation indices for predicting morphological traits. In addition, spatial sources of variation, such as soil structure heterogeneity, can disturb the selection process. This study compared the correlation between morphological traits and vegetation indices of elephant grass clones using basic linear mixed and spatial linear mixed models. In addition, we evaluated the magnitude and contribution of each index to explain the variations in traits and identify the best index for this forage. There was significant genetic variability in some morphological traits that enabled selection. Spatial models (autoregressive correlation among rows and columns) were more suitable for modeling some of the evaluated traits. There were changes in the magnitude of the correlation between traits when we considered the best-fit model instead of the non-spatial model. The increase in efficiency using the best-fitted model instead of the non-spatial model was 15.39% for heritability and 9.54% for accuracy. The total dry biomass was the only morphological trait significantly correlated with some vegetation indices, allowing for indirect selection. The coincidence index, heritability, and gains from indirect selection indicated that the normalized difference red-edge index was the best for selecting superior elephant grass high-yielding genotypes. The spatial modeling leveraged the genetic selection of high yield elephant grass genotypes for bioenergetic purposes.

A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data.

The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.

Classification of high-throughput phenotyping data for differentiation among nutrient deficiency in common bean.

The development of automated, image-based, high-throughput plant phenotyping enabled the simultaneous measurement of many plant traits. Big and complex phenotypic datasets require advanced statistical methods which enable the extraction of the most valuable traits when combined with other measurements, interpretation, and understanding of their (eco)physiological background. Nutrient deficiency in plants causes specific symptoms that can be easily detected by multispectral imaging, 3D scanning, and chlorophyll fluorescence measurements. Screening of numerous image-based phenotypic traits of common bean plants grown in nutrient-deficient solutions was conducted to optimize phenotyping and select the most valuable phenotypic traits related to the specific nutrient deficit. Discriminant analysis was used to compare the efficiency of groups of traits obtained by high-throughput phenotyping techniques (chlorophyll fluorescence, multispectral traits, and morphological traits) in discrimination between nutrients [nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), and iron (Fe)] at early and prolonged deficiency. Furthermore, a recursive partitioning analysis was used to select variables within each group of traits that show the highest accuracy for assigning plants to the respective nutrient deficit treatment. Using the entire set of measured traits, the highest classification success by discriminant function was achieved using multispectral traits. In the subsequent measurements, chlorophyll fluorescence and multispectral traits achieved comparably high classification success. Recursive partitioning analysis was able to intrinsically identify variables within each group of traits and their threshold values that best separate the observations from different nutrient deficiency groups. Again, the highest success in assigning plants into their respective groups was achieved based on selected multispectral traits. Selected chlorophyll fluorescence traits also showed high accuracy for assigning plants into control, Fe, Mg, and P deficit but could not correctly assign K and N deficit plants. This study has shown the usefulness of combining high-throughput phenotyping techniques with advanced data analysis to determine and differentiate nutrient deficiency stress.

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding

Late leaf spot (LLS), caused by Nothopassalora personata (Berk. & M.A Curt.), and groundnut rosette disease (GRD), [caused by groundnut rosette virus (GRV)], represent the most important biotic constraints to groundnut production in Uganda. Application of visual scores in selection for disease resistance presents a challenge especially when breeding experiments are large because it is resource-intensive, subjective, and error-prone. High-throughput phenotyping (HTP) can alleviate these constraints. The objective of this study is to determine if HTP derived indices can replace visual scores in a groundnut breeding program in Uganda. Fifty genotypes were planted under rain-fed conditions at two locations, Nakabango (GRD hotspot) and NaSARRI (LLS hotspot). Three handheld sensors (RGB camera, GreenSeeker, and Thermal camera) were used to collect HTP data on the dates visual scores were taken. Pearson correlation was made between the indices and visual scores, and logistic models for predicting visual scores were developed. Normalized difference vegetation index (NDVI) (r = –0.89) and red-green-blue (RGB) color space indices CSI (r = 0.76), v* (r = –0.80), and b* (r = –0.75) were highly correlated with LLS visual scores. NDVI (r = –0.72), v* (r = –0.71), b* (r = –0.64), and GA (r = –0.67) were best related to the GRD visual symptoms. Heritability estimates indicated NDVI, green area (GA), greener area (GGA), a*, and hue angle having the highest heritability (H2 > 0.75). Logistic models developed using these indices were 68% accurate for LLS and 45% accurate for GRD. The accuracy of the models improved to 91 and 84% when the nearest score method was used for LLS and GRD, respectively. Results presented in this study indicated that use of handheld remote sensing tools can improve screening for GRD and LLS resistance, and the best associated indices can be used for indirect selection for resistance and improve genetic gain in groundnut breeding.

QTL Mapping of Leaf Area Index and Chlorophyll Content Based on UAV Remote Sensing in Wheat

High-throughput phenotypic identification is a prerequisite for large-scale identification and gene mining of important traits. However, existing work has rarely leveraged high-throughput phenotypic identification into quantitative trait locus (QTL) acquisition in wheat crops. Clarifying the feasibility and effectiveness of high-throughput phenotypic data obtained from UAV multispectral images in gene mining of important traits is an urgent problem to be solved in wheat. In this paper, 309 lines of the spring wheat Worrakatta × Berkut recombinant inbred line (RIL) were taken as materials. First, we obtained the leaf area index (LAI) including flowering, filling, and mature stages, as well as the flag leaf chlorophyll content (CC) including heading, flowering, and filling stages, from multispectral images under normal irrigation and drought stress, respectively. Then, on the basis of the normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI), which were determined by multispectral imagery, the LAI and CC were comprehensively estimated through the classification and regression tree (CART) and cross-validation algorithms. Finally, we identified the QTLs by analyzing the predicted and measured values. The results show that the predicted values of determination coefficient (R2) ranged from 0.79 to 0.93, the root-mean-square error (RMSE) ranged from 0.30 to 1.05, and the relative error (RE) ranged from 0.01 to 0.18. Furthermore, the correlation coefficients of predicted and measured values ranged from 0.93 to 0.94 for CC and from 0.80 to 0.92 for LAI at different wheat growth stages under normal irrigation and drought stress. Additionally, a linkage map of this RIL population was constructed by 11,375 SNPs; eight QTLs were detected for LAI on wheat chromosomes 1BL, 2BL (four QTLs), 3BL, 5BS, and 5DL, and three QTLs were detected for CC on chromosomes 1DS (two QTLs) and 3AL. The closely linked QTLs formed two regions on chromosome 2BL (from 54 to 56 cM and from 96 to 101 cM, respectively) and one region on 1DS (from 26 to 27 cM). Each QTL explained phenotypic variation for LAI from 2.5% to 13.8% and for CC from 2.5% to 5.8%. For LAI, two QTLs were identified at the flowering stage, two QTLs were identified at the filling stage, and three QTLs were identified at the maturity stage, among which QLAI.xjau-5DL-pre was detected at both filling and maturity stages. For CC, two QTLs were detected at the heading stage and one QTL was identified at the flowering stage, among which QCC.xjau-1DS was detected at both stages. Three QTLs (QLAI.xjau-2BL-pre.2, QLAI.xjau-2BL.2, and QLAI.xjau-3BL-pre) for LAI were identified under drought stress conditions. Five QTLs for LAI and two QTLs for CC were detected by imagery-predicted values, while four QTLs for LAI and two QTLs for CC were identified by manual measurement values. Lastly, investigations of these QTLs on the wheat reference genome identified 10 candidate genes associated with LAI and three genes associated with CC, belonging to F-box family proteins, peroxidase, GATA transcription factor, C2H2 zinc finger structural protein, etc., which are involved in the regulation of crop growth and development, signal transduction, and response to drought stress. These findings reveal that UAV sensing technology has relatively high reliability for phenotyping wheat LAI and CC, which can play an important role in crop genetic improvement.