Biology subjects

Jubery, T. Z.

Publications and source records attributed to Jubery, T. Z..

6 recordsLinked to original sources

bioRxiv2025

AI-assisted Image-Based Phenotyping Reveals Genetic Architecture of Pod Traits in Mungbean (Vigna radiata L.)

Mungbean (Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel with 372 genotypes (2022-23) with AI-assisted image phenotyping using 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r>0.96 for pod length and seed per pod). Four complementary GWAS models identified 45 significant SNPs associated with pod curvature, length, width, and seed per pod traits. Notably, a significant SNP (5_35265704) on chromosome 1 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Vradi01g00001116, was located within the linkage disequilibrium (LD) region of this SNP, is part of the GH3 gene family, and has an Arabidopsis ortholog (AT4G27260) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 2, has been found to be significantly associated with both pod length and seed per pod. A candidate gene, Vradi02g00003971, located in the LD region of this SNP, belongs to the potassium transporter family and shares homology with the HAK5 gene family (AT4G13420) in Arabidopsis, which influences pod and seed growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, exhibiting an improvement of 12-22% over manual methods. These results demonstrate the potential of AI-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.

genomics↗

bioRxiv2025

Sewing the future of cotton: a multi-omics study combining nanomechanics, transcriptomics, and phenotypic traits

Cellulose microfibrils that are essential for mechanical strength and overall quality of cotton fibers. This study quantifies and compares the nanoscale structural and mechanical properties of cellulose microfibrils such as microfibril dimensions, crossover count and angles, roughness, and Youngs modulus for two popular cotton species: Gossypium hirsutum (Gh) and Gossypium barbadense (Gb) fibers across four growth stages (8, 12, 18, and 22 days post-anthesis) using atomic force microscopy (AFM). Our results revealed for the first time that Gb fibers exhibit a better alignment, finer dimensions, and higher stiffness compared to Gh fibers at nanoscale, resulting in smoother fiber surfaces, and improved quality at macroscale. We are also the first to develop machine-learning models to predict macroscale phenotypic traits specifically boll length and cellulose content using nanoscale features alone and in combination with multi-omics modalities, substantially enhancing the predictive accuracy and highlighting opportunities for robust cross-species modeling of cotton fiber traits.

biophysics↗

bioRxiv2023

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Hyperspectral reflectance data can be collected from large plant populations in a high-throughput manner in both controlled and field environments. The efficacy of using hyperspectral leaf reflectance as a proxy for traits that typically require significant labor and time to collect has been evaluated in a number of studies. Commonly, estimating plant traits using hyperspectral reflectance involves collecting substantial amounts of ground truth data from plant populations, which may not be feasible for many researchers. In this study, we explore the potential of data-driven approaches to analyze hyperspectral reflectance data with little to no ground truth phenotypic measurements. Evaluations were performed using data on the reflectance of 2,151 individual wavelengths of light from the leaves of maize plants harvested from 1,658 field plots of a replicated trial including representatives of 752 maize genotypes from the Wisconsin Diversity Panel. We reduced the dimensionality of this dataset using an autoencoder neural network and principal component analyses, producing 10 latent variables and principal components, respectively. A subset of these principal components and latent variables demonstrated significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by genetic factors. Moreover, correlations were observed between variables derived from the autoencoder network and principal components with molecular traits. Notably, the most relevant latent variable (LV8) showed a much stronger correlation with chlorophyll content (R2 = 0.59) compared to the most correlated principal component (PC2; R2 = 0.31). Furthermore, one latent variable exhibited modestly better performance than a partial least squares regression model in estimating leaf chlorophyll content (PLSR; R2 = 0.58, LV8; R2 = 0.59). A number of genetic markers in the maize genome were significantly correlated with variation in different latent variables in genome wide association studies. In a number of cases, significant signals in genome wide association studies were adjacent to genes with plausible links to traits expected to influence leaf hyperspectral reflectance patterns.

plant biology↗

bioRxiv2022

Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource-intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models. Here, we implement the popular self-supervised contrastive learning methods of NNCLR (Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellums size and for classifying haploid and diploid kernels. We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.

plant biology↗

bioRxiv2020

Using Machine Learning To Develop A Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum), and are an important structure where atmospheric nitrogen (N2) is fixed into bio-available ammonia (NH3) for plant growth and developmental. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is done on a less informative qualitative scale. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations; and has a prediction accuracy of 99%. SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficient soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

plant biology↗

bioRxiv2018

Measurements of maize root plasticity under water stress in hydroponic chamber

1.Under water stress, plants adjust root traits including depth of root system, root diameter, density of root per volume of soil, hydraulic conductance of root. In this experimental study, we present a method to quantify how hydraulic traits of maize roots adapt to drought. The experiments involve a microfluidic flow sensor and a custom-built pressure chamber, made of transparent plastic for visualization purposes. We measured how maize genotypes (PHB47 and PHZ51) grown for a week in deionized (DI) water and one day in hydroponic nutrients solution (called the irrigated condition) respond to one week of water stress. Conditions of water stress (called drought conditions) were created by mixing Polyethylene Glycol with the nutrients solution. Results show that under drought, the roots of both genotypes respond by approximately halving their global hydraulic conductance. This adjustment seems to be achieved mainly by reductions of the total surface area of the roots. Interestingly, the measured hydraulic conductivity of the roots grown under drought was significantly larger. In all, this study sheds light on how plants adapt to water stress in a hydroponic system, by decreasing root area and increasing root permeability.

physiology↗