scholarly journals Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population

2022 ◽  
Vol 12 ◽  
Author(s):  
Jana Ebersbach ◽  
Nazifa Azam Khan ◽  
Ian McQuillan ◽  
Erin E. Higgins ◽  
Kyla Horner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Breeding Population ◽  
Supervised Machine Learning ◽  
Oilseed Crop

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

scholarly journals Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress

2021 ◽  
Author(s):  
Nathan T Hein ◽  
Ignacio A Ciampitti ◽  
S V Krishna Jagadish
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Crop Improvement ◽  
Grain Filling ◽  
Time Of Day ◽  
Water Soluble ◽  
High Throughput Phenotyping ◽  
Improvement Programs

Abstract Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less-biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits or processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: i) time-of-day of flowering, to escape these stresses during flowering, ii) optimizing photosynthetic efficiency, iii) storage and translocation of water-soluble carbohydrates, and iv) yield and yield components to provide in-season yield estimates. An overview of current advances in remote sensing in capturing these traits, limitations with existing technology and future direction of research to develop high-throughput phenotyping approaches for these traits are discussed in this review. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

scholarly journals The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

2019 ◽  
Vol 63 (11) ◽  
pp. 1658-1667
Author(s):  
M J Castro-Bleda ◽  
S España-Boquera ◽  
J Pastor-Pellicer ◽  
F Zamora-Martínez
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Supervised Learning ◽  
Image Enhancement ◽  
Super Resolution ◽  
Supervised Machine Learning ◽  
Text Documents ◽  
Learning Techniques ◽  
Printed Text

Abstract This paper presents the ‘NoisyOffice’ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.

GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply

2017 ◽  
Vol 44 (1) ◽  
pp. 76 ◽  
Author(s):  
Tania Gioia ◽  
Anna Galinski ◽  
Henning Lenz ◽  
Carmen Müller ◽  
Jonas Lentz ◽  
...  
Keyword(s):  
High Throughput ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
Root Traits ◽  
Root Systems ◽  
Non Invasive ◽  
Large Sets ◽  
High Throughput Phenotyping ◽  
Improvement Programs ◽  
Root Phenotyping

New techniques and approaches have been developed for root phenotyping recently; however, rapid and repeatable non-invasive root phenotyping remains challenging. Here, we present GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system (4 plants min–1) based on flat germination paper. GrowScreen-PaGe allows the acquisition of time series of the developing root systems of 500 plants, thereby enabling to quantify short-term variations in root system. The choice of germination paper was found to be crucial and paper ☓ root interaction should be considered when comparing data from different studies on germination paper. The system is suitable for phenotyping dicot and monocot plant species. The potential of the system for high-throughput phenotyping was shown by investigating phenotypic diversity of root traits in a collection of 180 rapeseed accessions and of 52 barley genotypes grown under control and nutrient-starved conditions. Most traits showed a large variation linked to both genotype and treatment. In general, root length traits contributed more than shape and branching related traits in separating the genotypes. Overall, results showed that GrowScreen-PaGe will be a powerful resource to investigate root systems and root plasticity of large sets of plants and to explore the molecular and genetic root traits of various species including for crop improvement programs.

scholarly journals A workflow for characterizing nanoparticle monolayers for biosensors: Machine learning on real and artificial SEM images

2014 ◽  
Author(s):  
Adam Hughes ◽  
Zhaowen Liu ◽  
Maryam Raftari ◽  
Mark E Reeves
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Open Source Software ◽  
Materials Science ◽  
Sampling Bias ◽  
Supervised Machine Learning ◽  
Sem Images ◽  
Nanoscale Features ◽  
Nanoparticle Film

A persistent challenge in materials science is the characterization of a large ensemble of heterogeneous nanostructures in a set of images. This often leads to practices such as manual particle counting, and sampling bias of a favorable region of the “best” image. Herein, we present the open-source software, imaging criteria and workflow necessary to fully characterize an ensemble of SEM nanoparticle images. Such characterization is critical to nanoparticle biosensors, whose performance and characteristics are determined by the distribution of the underlying nanoparticle film. We utilize novel artificial SEM images to objectively compare commonly-found image processing methods through each stage of the workflow: acquistion, preprocessing, segmentation, labeling and object classification. Using the semi- supervised machine learning application, Ilastik, we demonstrate the decomposition of a nanoparticle image into particle subtypes relevant to our application: singles, dimers, flat aggregates and piles. We outline a workflow for characterizing and classifying nanoscale features on low-magnification images with thousands of nanoparticles. This work is accompanied by a repository of supplementary materials, including videos, a bank of real and artificial SEM images, and ten IPython Notebook tutorials to reproduce and extend the presented results.

scholarly journals Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Vahid Attari ◽  
Raymundo Arroyave
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Phase Field ◽  
Interface Energy ◽  
Energy Space ◽  
Supervised Machine Learning ◽  
Phase Field Method ◽  
Phase Field Modeling ◽  
Field Modeling ◽  
Multi Phase

AbstractComputational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5 intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rank σSI,σIL, and σIL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that shows σSI and σSL alter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

scholarly journals Prediction and characterization of transcription factors involved in drought stress response

2020 ◽  
Author(s):  
Chirag Gupta ◽  
Venkategowda Ramegowda ◽  
Supratim Basu ◽  
Andy Pereira
Keyword(s):  
Machine Learning ◽  
Transcription Factors ◽  
Drought Stress ◽  
Stress Response ◽  
Drought Tolerance ◽  
Web Application ◽  
Protein Structures ◽  
Gene Families ◽  
Supervised Machine Learning ◽  
Specific Gene

AbstractTranscription factors (TFs) play a central role in regulating molecular level responses of plants to external stresses such as water limiting conditions, but identification of such TFs in the genome remains a challenge. Here, we describe a network-based supervised machine learning framework that accurately predicts and ranks all TFs in the genome according to their potential association with drought tolerance. We show that top ranked regulators fall mainly into two ‘age’ groups; genes that appeared first in land plants and genes that emerged later in the Oryza clade. TFs predicted to be high in the ranking belong to specific gene families, have relatively simple intron/exon and protein structures, and functionally converge to regulate primary and secondary metabolism pathways. Repeated trials of nested cross-validation tests showed that models trained only on regulatory network patterns, inferred from large transcriptome datasets, outperform models trained on heterogenous genomic features in the prediction of known drought response regulators. A new R/Shiny based web application, called the DroughtApp, provides a primer for generation of new testable hypotheses related to regulation of drought stress response. Furthermore, to test the system we experimentally validated predictions on the functional role of the rice transcription factor OsbHLH148, using RNA sequencing of knockout mutants in response to drought stress and protein-DNA interaction assays. Our study exemplifies the integration of domain knowledge for prioritization of regulatory genes in biological pathways of well-studied agricultural traits.One Sentence SummaryNetwork-based supervised machine learning accurately predicts transcription factors involved in drought tolerance.

Phenotyping: New Windows into the Plant for Breeders

2020 ◽  
Vol 71 (1) ◽  
pp. 689-712 ◽  
Author(s):  
Michelle Watt ◽  
Fabio Fiorani ◽  
Björn Usadel ◽  
Uwe Rascher ◽  
Onno Muller ◽  
...  
Keyword(s):  
Complex Traits ◽  
Data Science ◽  
Phenotypic Diversity ◽  
Crop Improvement ◽  
High Capacity ◽  
Root Traits ◽  
Plant Phenotyping ◽  
Plant Structure ◽  
Breeding Populations ◽  
Field Phenotyping

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

Sign in / Sign up

Export Citation Format

Share Document