scholarly journals Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

2014 ◽  
Vol 165 (2) ◽  
pp. 506-518 ◽  
Author(s):  
Christian Klukas ◽  
Dijun Chen ◽  
Jean-Michel Pape
Keyword(s):  
Information System ◽  
Open Source ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Integrated Analysis ◽  
Source Information ◽  
Analysis Platform

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 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 Analysis of high-throughput plant image data with the information system IAP

2012 ◽  
Vol 9 (2) ◽  
pp. 16-18 ◽  
Author(s):  
Christian Klukas ◽  
Jean-Michel Pape ◽  
Alexander Entzian
Keyword(s):  
Information System ◽  
Image Analysis ◽  
Large Scale ◽  
Image Data ◽  
Analysis Data ◽  
Integrated Analysis ◽  
Current Form ◽  
Automated Generation ◽  
Plant Image ◽  
Analysis Platform

Summary This work presents a sophisticated information system, the Integrated Analysis Platform (IAP), an approach supporting large-scale image analysis for different species and imaging systems. In its current form, IAP supports the investigation of Maize, Barley and Arabidopsis plants based on images obtained in different spectra. Several components of the IAP system, which are described in this work, cover the complete end-to-end pipeline, starting with the image transfer from the imaging infrastructure, (grid distributed) image analysis, data management for raw data and analysis results, to the automated generation of experiment reports.

scholarly journals Perjalanan Penerapan Sistem Informasi Open Source Koha di Perpustakaan Universitas Muhammadiyah Surakarta dari tahun 2006 s.d. 2010

2015 ◽  
Vol 8 (1) ◽  
pp. 34
Author(s):  
Maria Husnun
Keyword(s):  
Information System ◽  
Open Source ◽  
English Language ◽  
Private University ◽  
Influential Factors ◽  
Source Information ◽  
Good Communication ◽  
Financial Condition

The title of the research is "The chronology of the application of open source information system Koha in the library of Muhammadiyah University of Surakarta from 2006 to 2010. "The background of this research is that the development of the information technology demands the library to use the technology. UMS' library was the first and its private-university library using Koha system as the automatic system in Indonesia. Koha's complete features and its development raised confusion UMS' library staff. The use od English language and the small number of the community of Koha users in Indonesia sometimes lead to misinterpretation.The method used in this research was the qualitative method, and its approach was the case study. The reason of using these was that the case happened was unique and the forms of the questions were how and why.The result of the research was that there were 3 periods of the chronology of Koha application in UMS library, namely: pre-Koha, whilst using Koha, and developing of Koha system. The application of Koha system in UMS library had been in accordance with the cycle of the development of the information system. This cycle includes influential factors were: (1) the rectorat's supports, (2) human resource and leadership, (3) trend in the library, (4) integration, (5) theneed of the system, (6) the data analyze, (7) conmunication, (8) good cooperation, (9) socialization, (10) open source, (11) financial condition, (12) the quality of the system, (13) the readiness of the facilities. Besides, the research found that were some factors as the theoretical contributors namely: good communication and cooperation, socialization, abd the readiness of the facilities. It also found that there were one was lost factor, namely: user interface.

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.

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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