scholarly journals The ‘PhenoBox’, a flexible, automated, open-source plant phenotyping solution

2018 ◽  
Vol 219 (2) ◽  
pp. 808-823 ◽  
Author(s):  
Angelika Czedik-Eysenberg ◽  
Sebastian Seitner ◽  
Ulrich Güldener ◽  
Stefanie Koemeda ◽  
Jakub Jez ◽  
...  
Keyword(s):  
Open Source ◽  
Plant Phenotyping ◽  
Source Plant

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.

scholarly journals AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Plant Methods ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Michael P. Pound ◽  
Susan Fozard ◽  
Mercedes Torres Torres ◽  
Brian G. Forde ◽  
Andrew P. French
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Analysis Approach ◽  
Plant Phenotyping

Research Findings

2005 ◽  
Vol 09 (06) ◽  
pp. 227-231
Keyword(s):  
Open Source ◽  
Susceptibility Gene ◽  
Source Plant ◽  
Research Findings

CAMBIA Reveals Open Source Plant Biotech For All. Japanese Researchers Find Susceptibility Gene for Arthritis. World's First Stroke Medicine.

scholarly journals CropSurveyor: a scalable open-source experiment management system for distributed plant phenotyping and IoT-based crop management

2018 ◽  
Author(s):  
Daniel Reynolds ◽  
Joshua Ball ◽  
Alan Bauer ◽  
Simon Griffiths ◽  
Ji Zhou
Keyword(s):  
Open Source ◽  
Management System ◽  
Field Experiments ◽  
Crop Management ◽  
Crop Growth ◽  
Plant Phenotyping ◽  
Smart Devices ◽  
Growth Monitoring ◽  
Crop Performance ◽  
Experiment Management

AbstractBackground:High-quality plant phenotyping and climate data lay the foundation of phenotypic analysis as well as genotype-by-environment interactions, which is important biological evidence not only to understand the dynamics between crop performance, genotypes, and environmental factors, but also for agronomists and farmers to monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies in recent years, many IoT-based remote sensing devices have been applied to phenotyping and crop monitoring that generate big plant-environment datasets every day; however, it is still technically challenging to calibrate, annotate, and aggregate big data effectively, especially when they were generated in multiple locations, and often at different scales.Findings:CropSurveyor is a PHP and SQL based server platform, which provides automated data collation, storage, device and experiment management through IoT-based sensors and distributed plant phenotyping workstations. It provides a two-component solution for monitoring biological experiments and networked devices, with interfaces specifically designed for distributed IoT devices and centralised data servers. Data transfer is performed automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSurveyor system, which synchronise daily representative crop growth images for quick and visual-based crop assessment, as well as detailed microclimate readings for GxE studies. CropSurveyor also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted.Conclusions:As an open-source experiment and data management system, CropSurveyor can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current data comparison through a single cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a PC, CropSurveyor has been used in wheat field experiments for prebreeding since 2016 and has the potential to enable scalable crop management and IoT-style agricultural practices in the near future.

Open Source Plant Breeding and the Open Source Seed Initiative

2016 ◽  
pp. 271-298 ◽  
Author(s):  
Claire H. Luby ◽  
Jack R. Kloppenburg ◽  
Irwin L. Goldman
Keyword(s):  
Plant Breeding ◽  
Open Source ◽  
Source Plant

scholarly journals Mobile Open-Source Plant-Canopy Monitoring System

2017 ◽  
Vol 06 (01) ◽  
pp. 1-13 ◽  
Author(s):  
Daniel K. Fisher ◽  
Yanbo Huang
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Plant Canopy ◽  
Source Plant

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.

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