RhizoVision Explorer: Open-source software for root image analysis and measurement standardization

Abstract Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter, and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed, and typically scanned on a flatbed scanner, and provides measurements like length, diameter, and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth, and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest, and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo TM, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo TM and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous, and tree species were used to compare results from RhizoVision Explorer with WinRhizo TM. The two software showed general agreement, except that WinRhizo TM substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements, and provide a foundation for collaborative improvement and reliable access to all.

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RhizoVision Explorer: Open-source software for root image analysis and measurement standardization

10.1101/2021.04.11.439359 ◽

2021 ◽

Author(s):

Anand Seethepalli ◽

Kundan Dhakal ◽

Marcus Griffiths ◽

Haichao Guo ◽

Gregoire T. Freschet ◽

...

Keyword(s):

Open Source ◽

Open Source Software ◽

Copper Wire ◽

Ground Truth ◽

Root Characteristics ◽

Root Volume ◽

Ground Truth Data ◽

Multiple Regions ◽

Image Set ◽

Different Diameters

AbstractRoots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter, and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing, and reliable measurements. The default broken roots mode is intended for roots sampled from pots or soil cores, washed, and typically scanned on a flatbed scanner, and provides measurements like length, diameter, and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth, and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest, and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a novel copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous, and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements, and provide a foundation for collaborative improvement and reliable access to all.Abstract Figure

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Eliciting Disease Data from Wikipedia Articles

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v8i1.6526 ◽

2016 ◽

Vol 8 (1) ◽

Author(s):

Geoffrey Fairchild ◽

Lalindra De Silva ◽

Sara Y. Del Valle ◽

Alberto M. Segre

Keyword(s):

Time Series ◽

Open Source ◽

Disease Surveillance ◽

Time Series Data ◽

Ground Truth ◽

Series Data ◽

Disease Detection ◽

Surveillance Systems ◽

Ground Truth Data ◽

Named Entity

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. This study presents the use of Wikipedia article content in this sphere. We demonstrate how a named-entity recognizer can be trained to tag case, death, and hospitalization counts in the article text. We also show that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.

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Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Sensors ◽

10.3390/s21196501 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6501

Author(s):

Michał Pełka ◽

Janusz Będkowski

Keyword(s):

Open Source ◽

Mechanical Design ◽

Laser Scanner ◽

Measurement Instrument ◽

Ground Truth ◽

Calibration Method ◽

Field Of View ◽

Ground Truth Data ◽

Calibration Process ◽

Data Registration

This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper.

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OPEN-SOURCE DIGITAL ELEVATION MODEL (DEMs) EVALUATION WITH GPS AND LiDAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w1-299-2016 ◽

2016 ◽

Vol XLII-4/W1 ◽

pp. 299-306 ◽

Cited By ~ 6

Author(s):

N. F. Khalid ◽

A. H. M. Din ◽

K. M. Omar ◽

M. F. A. Khanan ◽

A. H. Omar ◽

...

Keyword(s):

Open Source ◽

Digital Elevation Model ◽

Ground Truth ◽

Environmental Modeling ◽

Scale Factor ◽

Ground Truth Data ◽

Coastal Inundation ◽

Digital Elevation ◽

Vertical Accuracy ◽

Elevation Model

Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER GDEM), Shuttle Radar Topography Mission (SRTM), and Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) are freely available Digital Elevation Model (DEM) datasets for environmental modeling and studies. The quality of spatial resolution and vertical accuracy of the DEM data source has a great influence particularly on the accuracy specifically for inundation mapping. Most of the coastal inundation risk studies used the publicly available DEM to estimated the coastal inundation and associated damaged especially to human population based on the increment of sea level. In this study, the comparison between ground truth data from Global Positioning System (GPS) observation and DEM is done to evaluate the accuracy of each DEM. The vertical accuracy of SRTM shows better result against ASTER and GMTED10 with an RMSE of 6.054 m. On top of the accuracy, the correlation of DEM is identified with the high determination of coefficient of 0.912 for SRTM. For coastal zone area, DEMs based on airborne light detection and ranging (LiDAR) dataset was used as ground truth data relating to terrain height. In this case, the LiDAR DEM is compared against the new SRTM DEM after applying the scale factor. From the findings, the accuracy of the new DEM model from SRTM can be improved by applying scale factor. The result clearly shows that the value of RMSE exhibit slightly different when it reached 0.503 m. Hence, this new model is the most suitable and meets the accuracy requirement for coastal inundation risk assessment using open source data. The suitability of these datasets for further analysis on coastal management studies is vital to assess the potentially vulnerable areas caused by coastal inundation.

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OPEN-SOURCE IMAGE-BASED 3D RECONSTRUCTION PIPELINES: REVIEW, COMPARISON AND EVALUATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w17-331-2019 ◽

2019 ◽

Vol XLII-2/W17 ◽

pp. 331-338 ◽

Cited By ~ 8

Author(s):

E.-K. Stathopoulou ◽

M. Welponer ◽

F. Remondino

Keyword(s):

Open Source ◽

Point Cloud ◽

Large Scale ◽

Critical Evaluation ◽

Ground Truth ◽

Source Image ◽

Ground Truth Data ◽

Dense Point ◽

3D Information ◽

Image Orientation

Abstract. State-of-the-art automated image orientation (Structure from Motion) and dense image matching (Multiple View Stereo) methods commonly used to produce 3D information from 2D images can generate 3D results – such as point cloud or meshes – of varying geometric and visual quality. Pipelines are generally robust and reliable enough, mostly capable to process even large sets of unordered images, yet the final results often lack completeness and accuracy, especially while dealing with real-world cases where objects are typically characterized by complex geometries and textureless surfaces and obstacles or occluded areas may also occur. In this study we investigate three of the available commonly used open-source solutions, namely COLMAP, OpenMVG+OpenMVS and AliceVision, evaluating their results under diverse large scale scenarios. Comparisons and critical evaluation on the image orientation and dense point cloud generation algorithms is performed with respect to the corresponding ground truth data. The presented FBK-3DOM datasets are available for research purposes.

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EARBOX: An open tool for high-throughput measurements of the spatial organization of maize ears and inference of novel traits

10.1101/2021.12.20.473433 ◽

2021 ◽

Author(s):

Vincent Oury ◽

Timothe Leroux ◽

Olivier Turc ◽

Romain Chapuis ◽

Carine Palaffre ◽

...

Keyword(s):

Open Source ◽

High Throughput ◽

Spatial Organization ◽

Low Cost ◽

Plant Genetic Resources ◽

Ground Truth ◽

Ground Truth Data ◽

Spatial Features ◽

Novel Traits ◽

Maize Ear

Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organization, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

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