scholarly journals phenopype: a phenotyping pipeline for Python

2021 ◽  
Author(s):  
Moritz D Luerig
Keyword(s):  
Computer Vision ◽  
Data Collection ◽  
High Throughput ◽  
Digital Images ◽  
State Of The Art ◽  
High Dimensional ◽  
Phenotypic Data ◽  
Scientific Image ◽  
Phenotypic Information ◽  
High Throughput Phenotyping

Digital images are a ubiquitous way to represent phenotypes. More and more ecologists and evolutionary biologists are using images to capture and analyze high dimensional phenotypic data to understand complex developmental and evolutionary processes. As a consequence, images are being collected at ever increasing rates, already outpacing our abilities for processing and analysis of the contained phenotypic information. phenopype is a high throughput phenotyping package for the programming language Python to support ecologists and evolutionary biologists in extracting high dimensional phenotypic data from digital images. phenopype integrates existing state-of-the-art computer vision functions (using the OpenCV library as a backend), GUI-based interactions, and a project management ecosystem to facilitate rapid data collection and reproducibility. phenopype offers three different workflow types that support users during different stages of scientific image analysis (prototyping, low-throughput, and high-throughput). In the high-throughput workflow, users interact with human-readable YAML configuration files to effectively modify settings for different images. These settings are stored along with processed images and results, so that the acquired phenotypic information becomes highly reproducible. phenopype combines the advantages of the Python environment, with its state-of-the-art computer vision, array manipulation and data handling libraries, and basic GUI capabilities, which allow users to step into the automatic workflow when necessary. Overall, phenopype is aiming to augment, rather than replace the utility of existing Python CV libraries, allowing biologists to focus on rapid and reproducible data collection.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals OpenStats: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput Phenotypic Data

2020 ◽  
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C Mason ◽  
Ann-Marie Mallon ◽  
Damian Smedley ◽  
Terrence F Meehan ◽  
...  
Keyword(s):  
High Throughput ◽  
Software Package ◽  
Computational Time ◽  
Phenotypic Data ◽  
Creative Commons ◽  
International Mouse Phenotyping Consortium ◽  
Software Packages ◽  
Current Production ◽  
High Throughput Phenotyping ◽  
Reproducible Analysis

AbstractReproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

scholarly journals Correction to: Soybean iron deficiency chlorosis high throughput phenotyping using an unmanned aircraft system

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Austin A. Dobbels ◽  
Aaron J. Lorenz
Keyword(s):  
Iron Deficiency ◽  
Data Collection ◽  
Data Processing ◽  
High Throughput ◽  
Image Data ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft System ◽  
Iron Deficiency Chlorosis ◽  
High Throughput Phenotyping ◽  
Aircraft System

In the original article [1], under the subheading “Image data processing”, last paragraph, last sentence that reads as “The least …… data collection” was incorrectly published. The correct sentence should read as “Least-significant differences (P < 0.20) were calculated for all 36 trials on both ground-based and UAS-image based scores for both dates of data collection.” The original article has been corrected.

scholarly journals High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

2021 ◽  
Vol 8 ◽  
Author(s):  
Daoliang Li ◽  
Chaoqun Quan ◽  
Zhaoyang Song ◽  
Xiang Li ◽  
Guanghui Yu ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Agronomic Traits ◽  
Global Climate ◽  
Plant Phenotyping ◽  
Phenotypic Data ◽  
Operating Modes ◽  
Sensing Platform ◽  
Exploitation And Utilization ◽  
High Throughput Phenotyping

Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

scholarly journals Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges

2019 ◽  
Author(s):  
◽  
Dmitrii Yurievich Chemodanov
Keyword(s):  
Computer Vision ◽  
Data Collection ◽  
Shortest Path ◽  
State Of The Art ◽  
Cloud Infrastructure ◽  
Data Intensive Computing ◽  
Video Analytics ◽  
Service Chain ◽  
Data Intensive ◽  
Reliable Service

In the event of natural or man-made disasters, geospatial video analytics is valuable to provide situational awareness that can be extremely helpful for first responders. However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives.

Development of a Mobile Platform for Wheat Phenotyping

2020 ◽  
Author(s):  
Majid Khak Pour ◽  
Reza Fotouhi ◽  
Pierre Hucl
Keyword(s):  
Data Collection ◽  
High Throughput ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Mobile Platform ◽  
Mechatronic System ◽  
High Quality ◽  
Wheat Field ◽  
Crop Monitoring ◽  
High Throughput Phenotyping

Abstract Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, vegetation indices and is able to take high quality photos of crops. The system includes developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.

scholarly journals 227 Leveraging on high-throughput phenotyping technologies to optimize livestock genetic improvement and husbandry

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 55-55
Author(s):  
Guilherme J M Rosa ◽  
João R R Dorea ◽  
Arthur Francisco Araujo Fernandes ◽  
Tiago L Passafaro
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
High Throughput ◽  
Feed Intake ◽  
Genetic Improvement ◽  
Milk Composition ◽  
Sensor Technology ◽  
Efficient Data ◽  
High Throughput Phenotyping ◽  
Complex Relationships

Abstract The advent of fully automated data recording technologies and high-throughput phenotyping (HTP) systems has opened up a myriad of opportunities to advance breeding programs and livestock husbandry. Such technologies allow scoring large number of animals for novel phenotypes and indicator traits to boost genetic improvement, as well as for real-time monitoring of animal behavior and development for optimized management decisions. HTP tools include, for example, image analysis and computer vision, sensor technology for motion, sound and chemical composition, and spectroscopy. Applications span from health surveillance, precision nutrition, and control of meat and milk composition and quality. However, the application of HTP requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves large datasets with many covariates and complex relationships. In this talk we will discuss some of the challenges and potentials of HTP in livestock. Some examples to be presented include the utilization of automated feeders to record feed intake and to monitor feeding behavior in broilers, milk-spectra information to predict dairy cattle feed intake, and image analysis and computer vision to monitor growth and body condition in pigs and cattle. HTP and big data will become an essential component of modern livestock operations in the context of precision animal agriculture, boosting animal welfare, environmental footprint, and overall sustainability of animal production.

scholarly journals Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

2018 ◽  
Author(s):  
Malachy Campbell ◽  
Harkamal Walia ◽  
Gota Morota
Keyword(s):  
High Throughput ◽  
Legendre Polynomial ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Additive Genetic Variance ◽  
Second Order ◽  
Random Regression ◽  
Phenotypic Data ◽  
High Throughput Phenotyping ◽  
Time Point

AbstractThe accessibility of high-throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, have provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study is to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic prediction of shoot growth trajectories in rice (Oryza sativa). An estimate of shoot biomass, projected shoot area (PSA), was recored over a period of 20 days for a panel of 357 diverse rice accessions using an image-based greenhouse phenotyping platform. A RR that included a fixed second-order Legendre polynomial, a random second-order Legendre polynomial for the additive genetic effect, a first-order Legendre polynomial for the environmental effect, and heterogeneous residual variances was used to model PSA trajectories. The utility of the RR model over a single time point (TP) approach, where PSA is fit at each time point independently, is shown through four prediction scenarios. In the first scenario, the RR and TP approaches were used to predict PSA for a set of lines lacking phenotypic data. The RR approach showed a 11.6% increase in prediction accuracy over the TP approach. Much of this improvement could be attributed to the greater additive genetic variance captured by the RR approach. The remaining scenarios focused forecasting future phenotypes using a subset of early time points for known lines with phenotypic data, as well new lines lacking phenotypic data. In all cases, PSA could be predicted with high accuracy (r: 0.79 to 0.89 and 0.55 to 0.58 for known and unknown lines, respectively). This study provides the first application of RR models for genomic prediction of a longitudinal trait in rice, and demonstrates that RR models can be effectively used to improve the accuracy of genomic prediction for complex traits compared to a TP approach.

scholarly journals Automated high-throughput high-content autophagy and mitophagy analysis platform

2018 ◽  
Author(s):  
Jonathan Arias-Fuenzalida ◽  
Javier Jarazo ◽  
Jonas Walter ◽  
Gemma Gomez-Giro ◽  
Julia I. Forster ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
State Of The Art ◽  
Level Of Detail ◽  
Cell Populations ◽  
Current State ◽  
Pathological Conditions ◽  
High Throughput Phenotyping ◽  
Analysis Platform ◽  
High Throughput Manner

AbstractAutophagy and mitophagy play a central role in cellular homeostasis. In pathological conditions, the flow of autophagy and mitophagy can be affected at multiple and distinct steps of the pathways. Unfortunately, the level of detail of current state of the art analyses does not allow detection or dissection of pathway intermediates. Moreover, is conducted in low-throughput manner on bulk cell populations. Defining autophagy and mitophagy pathway intermediates in a high-throughput manner is technologically challenging, and has not been addressed so far. Here, we overcome those limitations and developed a novel high-throughput phenotyping platform with automated high-content image analysis to assess autophagy and mitophagy pathway intermediates.

scholarly journals OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242933
Author(s):  
Hamed Haselimashhadi ◽  
Jeremy C. Mason ◽  
Ann-Marie Mallon ◽  
Damian Smedley ◽  
Terrence F. Meehan ◽  
...  
Keyword(s):  
High Throughput ◽  
Software Package ◽  
Computational Time ◽  
Phenotypic Data ◽  
Creative Commons ◽  
International Mouse Phenotyping Consortium ◽  
Software Packages ◽  
Current Production ◽  
High Throughput Phenotyping ◽  
Reproducible Analysis

Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.

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