scholarly journals Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

2019 ◽  
Author(s):  
Simon Artzet ◽  
Tsu-Wei Chen ◽  
Jérôme Chopard ◽  
Nicolas Brichet ◽  
Michael Mielewczik ◽  
...  
Keyword(s):  
Open Source ◽  
High Throughput ◽  
Synthetic Data ◽  
3D Models ◽  
Plant Phenotyping ◽  
Published Data ◽  
Data Sets ◽  
Quantitative Measurements ◽  
Workflow System ◽  
Architectural Features

AbstractIn the era of high-throughput visual plant phenotyping, it is crucial to design fully automated and flexible workflows able to derive quantitative traits from plant images. Over the last years, several software supports the extraction of architectural features of shoot systems. Yet currently no end-to-end systems are able to extract both 3D shoot topology and geometry of plants automatically from images on large datasets and a large range of species. In particular, these software essentially deal with dicotyledons, whose architecture is comparatively easier to analyze than monocotyledons. To tackle these challenges, we designed the Phenomenal software featured with: (i) a completely automatic workflow system including data import, reconstruction of 3D plant architecture for a range of species and quantitative measurements on the reconstructed plants; (ii) an open source library for the development and comparison of new algorithms to perform 3D shoot reconstruction and (iii) an integration framework to couple workflow outputs with existing models towards model-assisted phenotyping. Phenomenal analyzes a large variety of data sets and species from images of high-throughput phenotyping platform experiments to published data obtained in different conditions and provided in a different format. Phenomenal has been validated both on manual measurements and synthetic data simulated by 3D models. It has been also tested on other published datasets to reproduce a published semi-automatic reconstruction workflow in an automatic way. Phenomenal is available as an open-source software on a public repository.

Estimation of Uncertainty Bounds From Cross Correlation Peak Ratio for Individual PIV Measurements

2012 ◽  
Author(s):  
John J. Charonko ◽  
Pavlos P. Vlachos
Keyword(s):  
Image Quality ◽  
Confidence Interval ◽  
Cross Correlation ◽  
Fluid Velocity ◽  
Synthetic Data ◽  
Data Sets ◽  
Correlation Peak ◽  
Individual Measurement ◽  
Quantitative Measurements ◽  
Piv Analysis

Numerous studies have established firmly that particle image velocimetry (PIV) is a robust method for non-invasive, quantitative measurements of fluid velocity, and that when carefully conducted, typical measurements can accurately detect displacements in digital images with a resolution well below a single pixel (in some cases well below a hundredth of a pixel). However, previously these estimates have only been able to provide guidance on the expected error for an average measurement under specific image quality and flow conditions. This paper demonstrates a new method for estimating the uncertainty bounds to within a given confidence interval for a specific, individual measurement. Here, the ratio of primary to secondary peak heights in a phase-only generalized cross-correlation is shown to correlate strongly with the range of observed error values for a given measurement, regardless of flow condition or image quality. Using an analytical model of the relationship derived from synthetic data sets, the uncertainty bounds at a 95% confidence interval are then computed for several artificial and experimental flow fields, and the resulting errors are shown to match closely to the predicted uncertainties. While this method stops short of being able to predict the true error for a given measurement, knowledge of the uncertainty level for a PIV experiment should provide great benefits when applying the results of PIV analysis to engineering design studies and CFD (computational fluid dynamics) validation efforts.

scholarly journals GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zane K. J. Hartley ◽  
Aaron S. Jackson ◽  
Michael Pound ◽  
Andrew P. French
Keyword(s):  
3D Reconstruction ◽  
Domain Adaptation ◽  
Synthetic Data ◽  
3D Models ◽  
Training Data ◽  
Plant Phenotyping ◽  
Size Estimation ◽  
Unsupervised Domain Adaptation ◽  
High Quality Image ◽  
Modelling Environment

3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.

scholarly journals Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning

2018 ◽  
Vol 315 (6) ◽  
pp. F1644-F1651 ◽  
Author(s):  
Susan M. Sheehan ◽  
Ron Korstanje
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Statistical Analysis ◽  
High Throughput ◽  
Quantitative Data ◽  
Human Interaction ◽  
Physiological Data ◽  
Data Sets ◽  
Quantitative Measurements ◽  
Consistent Manner

Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function.

scholarly journals OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis

2021 ◽  
Author(s):  
Landon Gary Alan Swartz ◽  
Suxing Liu ◽  
Drew Dahlquist ◽  
Emily S Walter ◽  
Skyler Kramer ◽  
...  
Keyword(s):  
Open Source ◽  
High Throughput ◽  
Modular Design ◽  
Morphological Changes ◽  
Gene Families ◽  
Plant Phenotyping ◽  
Gene Redundancy ◽  
High Throughput Screens ◽  
Changes Over Time ◽  
Inducible Gene

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, commercial HTPP platforms remain unaffordable. Here we describe the design and implementation of OPEN leaf, an open-source HTPP system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with the SMART imaging processing package was able to consistently document and quantify dynamic morphological changes over time at the whole rosette level and also at leaf-specific resolution when plants experienced changes in nutrient availability. The modular design of OPEN leaf allows for additional sensor integration. Notably, our data demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify characterize previously unidentified phenotypes in a leaf-specific manner.

scholarly journals Recent Data Augmentation Strategies for Deep Learning in Plant Phenotyping and Their Significance

2020 ◽  
Author(s):  
Douglas Pinto Sampaio Gomes ◽  
Lihong Zheng
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Plant Traits ◽  
Synthetic Data ◽  
Plant Phenotyping ◽  
Data Sets ◽  
Data Set ◽  
Augmentation Strategies ◽  
Augmentation Techniques ◽  
Benchmark Solutions

Plant phenotyping concerns the study of plant traits resulted from their interaction with their environment. Computer vision (CV) techniques represent promising, non-invasive approaches for related tasks such as leaf counting, defining leaf area, and tracking plant growth. Between potential CV techniques, deep learning has been prevalent in the last couple of years. Such an increase in interest happened mainly due to the release of a data set containing rosette plants that defined objective metrics to benchmark solutions. This paper discusses an interesting aspect of the recent best-performing works in this field: the fact that their main contribution comes from novel data augmentation techniques, rather than model improvements. Moreover, experiments are set to highlight the significance of data augmentation practices for limited data sets with narrow distributions. This paper intends to review the ingenious techniques to generate synthetic data to augment training and display evidence of their potential importance.

scholarly journals scAnt—an open-source platform for the creation of 3D models of arthropods (and other small objects)

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11155
Author(s):  
Fabian Plum ◽  
David Labonte
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Synthetic Data ◽  
3D Models ◽  
Depth Of Field ◽  
Multiple Perspectives ◽  
Automated Generation ◽  
Volume Measurements ◽  
The Creation ◽  
Area And Volume

We present scAnt, an open-source platform for the creation of digital 3D models of arthropods and small objects. scAnt consists of a scanner and a Graphical User Interface, and enables the automated generation of Extended Depth Of Field images from multiple perspectives. These images are then masked with a novel automatic routine which combines random forest-based edge-detection, adaptive thresholding and connected component labelling. The masked images can then be processed further with a photogrammetry software package of choice, including open-source options such as Meshroom, to create high-quality, textured 3D models. We demonstrate how these 3D models can be rigged to enable realistic digital specimen posing, and introduce a novel simple yet effective method to include semi-realistic representations of approximately planar and transparent structures such as wings. As a result of the exclusive reliance on generic hardware components, rapid prototyping and open-source software, scAnt costs only a fraction of available comparable systems. The resulting accessibility of scAnt will (i) drive the development of novel and powerful methods for machine learning-driven behavioural studies, leveraging synthetic data; (ii) increase accuracy in comparative morphometric studies as well as extend the available parameter space with area and volume measurements; (iii) inspire novel forms of outreach; and (iv) aid in the digitisation efforts currently underway in several major natural history collections.

scholarly journals RNA modification mapping with JACUSA2

2021 ◽  
Author(s):  
Michael Piechotta ◽  
Qi Wang ◽  
Janine Altmueller ◽  
Christoph Dieterich
Keyword(s):  
High Throughput ◽  
Comprehensive Analysis ◽  
Hek293 Cells ◽  
Rna Modification ◽  
Published Data ◽  
Data Sets ◽  
Analysis Framework ◽  
Sequencing Data

A whole series of high-throughput antibody-free methods for RNA modification detection from sequencing data emerged lately. We present JACUSA2 as a versatile software solution and comprehensive analysis framework for RNA modification detection assays that are based on either the Illumina or Nanopore platform. Importantly, JACUSA2 can integrate information from multiple experiments (e.g. replicates and different conditions) and different library types (e.g. first- or secondstrand libraries). We demonstrate its utility by example, showing three analysis workflows for m6A detection on published data sets: 1) MazF m6a-sensitive RNA digestion (FTO+ vs FTO-), 2) DART-seq (YTHwt vs YTHmut) and 3) Nanopore profiling (METTL3 +/+ vs -/-). All assays have been conducted in HEK293 cells and complement one another.

scholarly journals New analysis pipeline for high-throughput domain–peptide affinity experiments improves SH2 interaction data

2020 ◽  
Vol 295 (32) ◽  
pp. 11346-11363
Author(s):  
Tom Ronan ◽  
Roman Garnett ◽  
Kristen M. Naegle
Keyword(s):  
High Throughput ◽  
Measurement Techniques ◽  
False Negative ◽  
Sh2 Domain ◽  
Positive Interactions ◽  
Published Data ◽  
Data Sets ◽  
Interaction Data ◽  
Analysis Pipeline ◽  
Domain Peptide

Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2–pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2–pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2–pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2–pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain–peptide interactions.

scholarly journals PhenoImage: an open-source GUI for plant image analysis

2020 ◽  
Author(s):  
Feiyu Zhu ◽  
Manny Saluja ◽  
Jaspinder Singh ◽  
Puneet Paul ◽  
Scott E. Sattler ◽  
...  
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Plant Species ◽  
High Throughput ◽  
Large Scale ◽  
Imaging System ◽  
Crop Improvement ◽  
Plant Phenotyping ◽  
High Throughput Phenotyping ◽  
Improvement Programs

AbstractHigh-throughput genotyping coupled with molecular breeding approaches has dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into the development of large-scale downstream processing of the imaging datasets derived from high-throughput phenotyping (HTP) platforms. However, most available tools require some programing skills. We developed PhenoImage – an open-source GUI based cross-platform solution for HTP image processing with the aim to make image analysis accessible to users with either little or no programming skills. The open-source nature provides the possibility to extend its usability to meet user-specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high-performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived RGB and fluorescence shoot images from two plant species: sorghum and wheat differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage.HighlightPhenoImage is an open-source application designed for analyzing images derived from high-throughput phenotyping.

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