skip to main content


Search for: All records

Award ID contains: 2102120

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Introduction

    Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras.

    Methods

    In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions.

    Results

    Two different classifiers were trained to predict the plant treatment—random forest and multilayer perceptron neural networks—finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyllafluorescence, to provide more insights and possible explanations about the classification outputs.

    Discussion

    Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.

     
    more » « less
    Free, publicly-accessible full text available February 21, 2025
  2. Abstract

    Cotton (Gossypium hirsutumL.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (‘UGA230’, ‘UA48’ and ‘CSX8308’) and updating the ‘TM-1’ cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from ‘Pima’ cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

     
    more » « less
    Free, publicly-accessible full text available May 30, 2025
  3. Abstract

    Nondestructive plant phenotyping forms a key technique for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-throughput phenotyping facilities can further our understanding of plant development and stress responses, their high costs greatly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration. We paired these devices to a suite of computational pipelines for integrated and straightforward data analysis. The developed tools were validated for their suitability for large genetic screens by evaluating a cowpea (Vigna unguiculata) diversity panel for responses to drought stress. The observed natural variation was used as an input for a genome-wide association study, from which we identified nine genetic loci that might contribute to cowpea drought resilience during early vegetative development. The homologs of the candidate genes were identified in Arabidopsis (Arabidopsis thaliana) and subsequently evaluated for their involvement in drought stress by using available T-DNA insertion mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating the identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of plant species.

     
    more » « less
    Free, publicly-accessible full text available May 3, 2025
  4. Abstract

    Stem water potential (Ψstem) is a key indicator for assessing plant water status, which is crucial in understanding plant health and productivity. However, existing measurement methods for Ψstem, characterized by destructiveness and intermittency, limit its applicability. Microtensiometers, an emerging plant-based sensor, offer continuous monitoring capabilities and have shown success in certain vine and tree species. In this study, we investigate the efficacy of microtensiometers ability to monitor the Ψstemof cotton (Gossypium hirsutumL.) under three distinct irrigation treatments in Maricopa, Arizona, an extremely hot, arid environment. We analyze the diurnal dynamics of Ψstemacross the irrigation regimes and compare these measurements with midday leaf water potentials (Ψleaf) obtained using a dewpoint potentiometer. Our results demonstrate that the microtensiometer-derived Ψstemclosely follows known diurnal patterns of Ψleaf, tracking with vapor pressure deficit (VPD) and responding to variations in irrigation levels and soil moisture content. Time cross-correlation analysis reveals an 80-minute lag in Ψstemresponse to changing VPD under non-water limiting conditions, which shortens under water-limiting conditions. Additionally, we establish a robust linear relationship (R2adj = 0.82) between Ψstemand Ψleaf, with this relationship strengthening as water availability decreases. Notably, we observe mean gradients of 1.2 and 0.06 MPa between soil vs. stem and stem vs. leaf water potentials, respectively. Moreover, Ψstemdata proves to be more sensitive in distinguishing between irrigation treatments earlier in the growing season compared to Ψleaf, leaf temperature and leaf gas exchange parameters. These findings highlight the utility of microtensiometers as valuable tools for monitoring water status in smaller-stemmed row crops such as cotton.

     
    more » « less
    Free, publicly-accessible full text available April 10, 2025
  5. Summary

    Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fibre yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well‐watered and water‐limited conditions in the Arizona low desert were sequenced. Gene co‐expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A‐A and GhHSFA6B‐D, associated with improved yield under water‐limited conditions in an ABA‐independent manner. DNA affinity purification sequencing (DAP‐seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint yield‐associated genes previously identified through genome‐wide association studies (GWAS)‐based approaches,GhABP‐DandGhIPS1‐A. Biochemical and phylogenetic approaches were used to determine thatGhIPS1‐Ais positively regulated by GhHSFA6B‐D, and that this regulatory mechanism is specific toGossypiumspp. containing the A (old world) genome. Finally, an SNP was identified within the GhHSFA6B‐D binding site inGhIPS1‐Athat is positively associated with yield under water‐limiting conditions. These data lay out a regulatory connection between abiotic stress and fibre yield in cotton that appears conserved in other systems such as Arabidopsis.

     
    more » « less
  6. Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent,Macrophomina phaseolina(Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

     
    more » « less
    Free, publicly-accessible full text available June 27, 2025
  7. As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area). 
    more » « less