Volume 11, Issue 5 e11545
Open Access
Open DataOpen Material

FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes

William N. Weaver

Corresponding Author

William N. Weaver

Department of Ecology and Evolutionary Biology, University of Michigan, 1105 N. University Ave., Ann Arbor, 48109 Michigan, USA

Correspondence William N. Weaver, Department of Ecology and Evolutionary Biology, University of Michigan, 1105 N. University Ave., Ann Arbor, Michigan 48109, USA.

Email: [email protected]

Contribution: Conceptualization, Data curation, Formal analysis, Funding acquisition, ​Investigation, Methodology, Software, Validation, Visualization, Writing - original draft

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Stephen A. Smith

Stephen A. Smith

Department of Ecology and Evolutionary Biology, University of Michigan, 1105 N. University Ave., Ann Arbor, 48109 Michigan, USA

Contribution: Funding acquisition, Methodology, Supervision, Writing - review & editing

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First published: 28 September 2023
Citations: 2

This article is part of the special issue “Advances in Plant Imaging across Scales.”



Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers.

Methods and Results

Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi–based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device.


FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.

In contemporary specimen collection, the integration of multiple digital data sources necessitates the utilization of advanced processing techniques to consolidate notes, observations, photographs, and specimens into a comprehensive and cohesive data set. Fortunately, software packages can streamline this cumbersome task. There are apps to help organize field observations (Maya-Lastra, 2016; Powell et al., 2019; Seltzer, 2019), manage ecological surveys (Aanensen et al., 2009; Anastácio et al., 2017; Esri, 2019; Nowak et al., 2020), and measure specimens (Easlon and Bloom, 2014; Schrader et al., 2017; Weaver et al., 2020). However, the curation of digital images captured during fieldwork has received little attention.

Researchers frequently photograph specimens to document an occurrence or observation, but these images can often also be rich data sources of trait measurements for the original collector or other researchers. For example, herbarium specimen vouchers are widely used in the natural sciences to chronicle climate change (Calinger, 2013; Willis et al., 2017b; Jones and Daehler, 2018; Meineke et al., 2018), ground plant systematics and taxonomy (Stuessy, 1975; Wen et al., 2015; Espinosa and Pinedo Castro, 2018), inform conservation initiatives (Greve et al., 2016; Nualart et al., 2017; Lughadha et al., 2018; Rocchetti et al., 2021), and provide a wealth of trait data for phylogenetic and phenological studies (Savolainen et al., 1995; Edwards and Smith, 2010; Bakker, 2017; Willis et al., 2017a; Mishler et al., 2020; Folk et al., 2021). Vouchers are particularly useful because of their standardization and availability. However, herbarium specimens are not without their challenges. Preservation and mounting techniques can often distort or obscure phenological and morphological traits, particularly those related to three-dimensional structure, while colors may also fade and change as specimens dry and age (Willis et al., 2017a). In addition to vouchers, images taken at the time of collection can serve as supplementary information to the extended specimen record, allowing for a more complete understanding of specimens and their conditions at the time of collection (Baskauf and Kirchoff, 2008; Gómez-Bellver et al., 2019; Hardisty et al., 2022). Platforms like iNaturalist have led to an explosion of digital-only records that are regularly used to study phenology, species distributions, conservation, and climate change, and can be linked to physical vouchers (Heberling and Isaac, 2018). If these field images were standardized and metrically validated, they could also be used for quantitative trait data collection.

Several authors have established standardized protocols for capturing images of live plants that can serve as a supplement or substitute for physical herbarium specimens. The term “photo vouchers” is defined by Funk et al. (2017) as a collection of photographs of a plant that facilitates identification and documents its presence when the collection of a physical specimen is not possible. Gómez-Bellver et al. (2019) provide a definition for “fusion vouchers” as a compilation of images attached to a standard herbarium sheet that enhances a physical specimen by providing supplementary contextual information when its size, structure, toxicity, fragility, or special status requires further representation. While these protocols provide valuable context and content that can provide a more comprehensive understanding of plant habit, color, structure, and identification, they offer limited utility for measuring morphological traits. Quantitative traits are increasingly used in a wide array of biological research, but most field images lack photogrammetric scale bars required for accurate measurements.

To enable quantitative trait data collection from field images, we introduce a new specimen documentation method called “snapshot vouchers.” This method involves capturing images of living plant organs against a photogrammetric background, enabling precise measurement of morphological traits. Snapshot vouchers can enhance physical vouchers or serve as digital-only records. By incorporating a photogrammetric background, iNaturalist photos can also qualify as snapshot vouchers.

To realize the full potential of snapshot vouchers and field images we created FieldPrism, a flexible software and hardware suite designed to streamline image processing and management. FieldPrism addresses common workflow bottlenecks by providing automatic image skew, yaw, and pitch correction; pixel-to-metric conversion; file renaming; and metadata collection tools. Optionally, FieldPrism can also address the administerial task of organizing and renaming hundreds or thousands of uninformatively named image files, an otherwise laborious and error-prone procedure (Forssman et al., 2016). Handwritten labels in images are not readily machine readable, so we employ QR codes to discriminate between collections. QR codes are a popular organizational tool in biological research and have been used to manage DNA extraction from herbarium specimens (Folk et al., 2021), manage museum collections (Sendino, 2013), link specimens with their digital metadata (Diazgranados and Funk, 2013), and link iNaturalist records to physical specimens (Heberling and Isaac, 2018).

Given the unpredictability of specimen collection in the field and the wide range of project scopes and complexities, we saw the need for a flexible and scalable system for identifying snapshot vouchers. This motivated the development of our QR code flipbook where users can assign QR codes to different species, sites, days, leaves on a plant, or other collection identifiers. For example, the QR codes for numbers 1–5 may correspond to five different species, while the codes for letters A–E may correspond to different locations. When the user takes a picture of species 1 at location A, they simply flip to those codes, take the picture with those codes in view, and continue collecting. Alternatively, QR codes can directly contain predefined collection identifiers, like the species name, collector's name, or location. FieldPrism will organize and rename the photos accordingly. The flipbook can then be reused on another trip where the numbers and letters will refer to other species and locations.

Archival images of herbarium specimen vouchers typically include a scale bar, which is a key feature often missing or incorrectly utilized in field images because they are often skewed and not rectilinear. If the camera is not positioned perpendicular to the subject or the scale bar is not on the same plane as the subject, its usefulness in obtaining accurate measurements is significantly limited. Additionally, converting pixel distance into metric distance requires a manual process, such as using ImageJ (Rueden et al., 2017) to measure the scale bar. With FieldPrism, we developed a simple photogrammetric background template that, in conjunction with our machine learning and computer vision algorithms, allows for the automated conversion of pixels to metric units. By using this template, FieldPrism can correct a variety of image distortions, including skew, yaw, and pitch, and produce a rectilinear snapshot voucher that compensates for cases in which the camera was not positioned perpendicularly to the subject.

Our tools can be used either independently or together depending on the project requirements. For instance, the QR code flipbook can be used to organize snapshot vouchers on a specimen collection trip, but it may not be necessary for an iNaturalist collection. FieldPrism methods are compatible with any digital camera, including smartphones. However, for additional utility, we developed a mobile imaging apparatus called FieldStation that enhances imaging ergonomics, operates without an internet connection, captures GPS coordinates, and saves all data redundantly to multiple USB storage devices. FieldStation's monitoring and control interface is accessible via any smartphone, tablet, or computer, but it can also be operated without a monitor using physical controls and audio feedback. With the goal of supporting the generation of quantitative trait data, FieldPrism uniformly curates snapshot vouchers and emulates the standardization of physical herbarium specimen vouchers.


Below, we provide an overview of each FieldPrism component and outline possible use cases. First, we describe the photogrammetric background template, which is foundational for producing snapshot vouchers. Then, we explore three possible ways to use our QR code system as part of a specimen identification hierarchy and how to organize the QR codes in a flipbook. Finally, we describe the FieldStation and the FieldPrism software and test our methods under a variety of conditions.

Photogrammetric background template

In many disciplines, scale bars and rulers routinely accompany the subject matter to provide a notion of scale or enable pixel-to-metric conversion a posteriori. We developed a photogrammetric background template (FieldSheet) to enable precise pixel-to-metric conversion and image distortion correction (Figure 1, Figure 2A). The simplest FieldPrism collection workflow involves attaching a FieldSheet to a clipboard, placing the subject onto the FieldSheet, and taking a photo with a digital camera (e.g., a smartphone; Figure 1A). The FieldSheet consists of four markers, each containing four 1-cm squares, and includes a 10-cm line for reference. Users can print copies of our FieldSheet using any standard printer onto six common page sizes: A3, A4, A5, Tabloid, Letter, and Legal. FieldSheet templates can be downloaded from https://www.fieldprism.org, or users can customize FieldSheets by editing the “FieldSheetBuilder.yaml” configuration file (see Data Availability Statement). If even larger templates are required, the four corner markers can be cut out and mounted on a larger surface, with care to maintain rectilinear placement. This custom size is supported in the FieldPrism software by updating the “custom” template dimensions. We validated a large custom FieldSheet using a 55 cm × 71 cm white hobby poster board with FieldSheet markers pasted in each corner.

Details are in the caption following the image
Four different ways to use FieldPrism. (A) Including FieldSheets in citizen science initiatives, iNaturalist collections, or general field images can extend the utility of the images beyond their original scope. (B) Pairing a QR code flipbook with FieldSheets enables specimen curation and machine-readable identification for a variety of research. (C) Our mobile imaging system, FieldStation, improves imaging ergonomics, logs GPS coordinates, saves data redundantly, and increases standardization. (D) Our FieldPrism software can correct image and calculate a pixel-to-metric conversion ratio for any image that includes a FieldSheet. QR codes within an image can be decoded to generate a unique specimen identifier for renaming image files.
Details are in the caption following the image
FieldPrism components. (A) Five premade FieldSheet options are available, ranging in size from A5 to A4, Legal, A3, and even a custom poster board, to demonstrate non-standard use cases. Tabloid and Letter sizes are not pictured. (B) An example of a QR code flipbook with six hierarchical levels printed on waterproof package labels attached to a waterproof memo-sized notebook. (C) Three of the cameras used to validate FieldPrism, from left to right, are a Lumix G9, Samsung Galaxy S9+ , and OAK-1. (D) Two FieldStation mobile imaging systems are shown, one designed for A4 or Letter-sized FieldSheets (left) and the other for A3 or Tabloid-sized FieldSheets and equipped with a smartphone monitor holder (right). The internal components, such as the Raspberry Pi 4, battery banks, GPS module, and USB storage devices, are also visible in the bottom half of both images. Numbers in the images correspond to specific components.

When printing FieldSheets, it is critical to verify that the printer did not improperly scale the PDF. Users can manually measure the 10-cm line or compare the size of a credit card with the accompanying size validation sheet and set the printer to 100% scaling if necessary. After verification, the FieldSheet can be attached to a clipboard and is ready for use. We recommend printing FieldSheets on waterproof paper or the waterproof shipping labels (e.g., Avery 5526 Waterproof Film Shipping Labels; Avery Products Corporation, Brea, California, USA) and sticking the whole sheet onto a plastic or metal clipboard. Make sure that the subject of each photograph is positioned at the center of the FieldSheet and does not cover the markers, and take care to ensure that it is in the same plane or as close as possible, especially for subjects with three-dimensional structure. Images that include our FieldSheet can be processed with the FieldPrism software to produce curated snapshot vouchers.


To ensure that FieldPrism is broadly accessible, we validated the FieldPrism software with images taken using smartphone cameras (Android and Apple), mirrorless and digital single-lens reflex (DSLR) professional cameras with a variety of lenses, and an OAK-1 machine vision camera (Luxonis, Littleton, Colorado, USA) (Table 1). Each camera produced usable snapshot vouchers. While FieldPrism can correct skewed images, it cannot correct distortion introduced by fisheye or wide-angle lenses. Wide-angle lenses should be avoided because they produce curvilinear images that distort metric increments across the image. If the manufacturer provides a lens distortion correction profile, it should be applied to the batch of images prior to using FieldPrism to correct skew, yaw, and pitch distortions. If the specimen contains small features or if the FieldSheet is far from the camera and occupies a fraction of the frame (see Figure 3, row 4), then consider using a camera with a sensor resolution greater than 20 megapixels to retain detail.

Table 1. A list of cameras used for development and testing. For validation testing, cameras with manual modes were set to an aperture of f/5.6, ISO 800, and a shutter speed of 1/125 s.
Camera Megapixels Focal distance (mm)a Horizontal field of view (degrees) Aperture ISO Shutter speed (s)
OAK-1 12 35* 54 f/2.0 Auto Auto
Samsung Galaxy S9+ 12 26* 65 f/1.5-2.4 Auto Auto
Panasonic Lumix G9 20.3 50* 40 f/5.6 800 1/125
Canon R5 45 50 40 f/5.6 800 1/125
Nikon D850b 45 50 40 f/5.6 Auto Auto
iPhoneb 12 26* 65 f/1.8 Auto Auto
  • a Focal distances with an asterisk are the full-frame equivalent focal distances, as those cameras have smaller sensors.
  • b Images taken by these cameras were used to train the machine learning algorithm but were not part of validation testing.
Details are in the caption following the image
Tracking five images taken with five different cameras and four different FieldSheet sizes as they are processed by FieldPrism. Processed images (columns B–G) are unmodified FieldPrism output images. Row 2 shows an image from our field test using a prototype FieldSheet and QR code, provided by colleagues at the Morton Arboretum. (A) The original unprocessed image. (B) Overlay image showing photogrammetric markers and QR codes identified by our machine learning network. (C) Distortion-corrected image. (D) Top-left justified distortion-corrected image. (E) Uniformly justified distortion-corrected image. (F) Uniformly justified distortion-corrected image with machine learning prediction overlay. (G) Uniformly justified distortion-corrected image with pixel-to-metric conversion verification overlay. Using the conversion ratio, a red 1-cm square and green 10-cm line are overlayed on the FieldSheet for visual confirmation. Users can select between image versions C, D, and E as the final corrected image. Options C and D maintain the original pixel resolution of the image, while option E allows users to configure the final image dimensions (e.g., 3000 × 4000 pixels), producing a uniform processed data set.

QR codes and specimen identification hierarchy

To fully take advantage of our QR code system for generating unique specimen names for collections projects, users must define a specimen identification hierarchy before venturing into the field. A specimen identification hierarchy is a modular organizational scheme that enables users to define short identifiers that can be easily translated into QR codes (Table 2). A project's specimen identification hierarchy is prepared as a CSV file where each column corresponds to a hierarchical level and each row is an identifier component of the parent hierarchical level. We recommend using no more than six hierarchical levels because using more would make the QR code flipbook impractical, but there is no upper limit to the number of variants (rows) per level.

Table 2. Three example tables used to build QR codes for the QR code flipbook. The cells shaded gray represent the exact text input used in the FieldPrism QR code generator. Entries below headers following the pattern “Level_1” are encoded directly into the QR code. Entries below headers following the pattern “Level_1_Alias” will be used to generate the final specimen identifier and filename by translating the encoded value (the cell to the left). (A) The “determinate” table demonstrates a workflow where all names are known prior to collection, aliases are not used. (B) The “indeterminate” table demonstrates a workflow where QR codes contain placeholder values; alias names are assigned to each placeholder value at the time of collection and are added to the corresponding alias cell after collection. (C) The “hybrid” table demonstrates a workflow that uses a combination of determinate and indeterminate labels.
QR code usage Columns representing hierarchical levels
A. Determinatea Level_1 Level_2 Level_3 Level_4 Level_5 Level_6
Site-1 Day-1 Collector-1 Quercus macrocarpa sun abaxial
Site-2 Day-2 Collector-2 Quercus alba shade adaxial
Site-3 Day-3 Collector-3 Quercus velutina    
Site-4   Collector-4 Quercus rubra    
B. Indeterminate with Aliasb Level_1 Level_1_Alias Level_2 Level_2_Alias Level_3 Level_3_Alias
A Quercus macrocarpa 1 sun a abaxial
B Quercus alba 2 shade b adaxial
C Quercus velutina 3 both c both
D Quercus rubra 4 unknown    
E Quercus gambelii        
F Quercus bicolor        
C. Hybridc Level_1 Level_1_Alias Level_2 Level_2_Alias Level_3 Level_3_Alias
Site-1 Site-1 A Quercus alba sun sun
Site-2 Site-2 B Quercus rubra shade shade
Site-3 Site-3 C Quercus gambelii    
Site-4 Site-4 D Quercus bicolor    
  • a Example reconstructed determinate image identifiers: Site-2_Day-3_Collector-4_Quercus velutina_sun_adaxial, Site-3_Day-2_Collector-3_Quercus rubra_shade_adaxial.
  • b Example reconstructed indeterminate image identifiers: Quercus alba_sun_adaxial, Quercus velutina_shade_.
  • c Example reconstructed hybrid image identifiers: Site-2_Quercus alba_shade, Site-3_Quercus rubra_sun.

Users have three options for defining a specimen identification hierarchy: (1) predefining the names of specimen identifier components, (2) using placeholder values for specimen identifier components when the names of specimens or locations cannot be anticipated, or (3) applying a hybrid implementation with some predefined names and some placeholder names. Placeholder components allow for indeterminate imaging schemes. For example, a field expedition is unlikely to know which species or how many individuals they will need to photograph. In this case, a placeholder QR code “A” could represent “Quercus macrocarpa” (Table 2B). While in the field, write down “Quercus macrocarpa” next to the “A” QR code in the flipbook and then add this alias to the naming hierarchy CSV file as the alias to “A”; FieldPrism will then know to translate “A” accordingly. A mixture of determinate and indeterminate components can also be used in a hybrid format. Additional specimen identification hierarchy examples can be found in Table 2.

QR code flipbook

After defining a specimen identification hierarchy, the FieldPrism QR code generator will create printable PDFs containing either 24 or 40 QR codes per sheet depending on the length of the label text. To create a flipbook, users cut out each QR code and attach them to a small memo-sized notebook so that each page contains only one variant per hierarchical level (Figure 2B). Users then cut the notebook page between each QR code. The result is an organized flipbook that can display any combination of variants in the defined naming hierarchy. We strongly recommend printing QR codes onto the previously mentioned waterproof package shipping labels. These labels prevent ink bleeding, are weather resistant, and conveniently attach to Rite-In-The-Rain notebooks (Rite in the Rain, Tacoma, Washington, USA). Printing QR codes with toner onto regular paper is discouraged due to smearing. We provide two predefined QR code templates that will accommodate most use cases, but users can edit the “FieldSheetBuilder.yaml” configuration file to customize the QR codes (e.g., shrinking the codes to fit 64 per sheet).


Holding a clipboard while setting the QR code flipbook, adjusting the specimen, and taking a photo with a smartphone or professional camera is an ungraceful ballet at best and may even require two people. To ameliorate poor ergonomics, we created FieldStation, a mobile imaging apparatus that can be used in conjunction with the FieldPrism software and FieldSheets (Figure 2D). FieldStation is built around a Raspberry Pi 4 Model B (https://www.raspberrypi.com; Raspberry Pi Foundation, Cambridge, United Kingdom) and an OpenCV OAK machine vision camera and can be assembled using off-the-shelf components in less than two hours for approximately $600 USD, depending on the configuration (Appendix S1; see Supporting Information with this article). We used a storage clipboard to contain the Raspberry Pi, batteries, USB storage, and GPS module. A microphone stand is attached to the top of the clipboard to hold the OAK-1 camera and GPS antenna. A mini six-key keyboard is used to control the camera and GPS. For monitor-free use, headphones can be plugged into the Raspberry Pi; different tones will sound to indicate successful operations or errors. Camera straps are attached to the sides of the clipboard, improving ergonomics by allowing the user to wear the imaging apparatus, leaving hands free for other tasks, and ensuring that the camera is aligned with the FieldSheet.

Using FieldStation

The FieldStation GUI allows the user to control and monitor the imaging process. A focus check window shows a cropped portion of the live camera feed, while the larger window displays the saved image. In the right panel, various verification statuses are displayed. We use Laplacian variance to detect blurry and out-of-focus images and give the user a chance to take a sharp photo. The suffix “_b” is optionally added to all blurry photos for quick quality control. GPS information is also displayed here and can be refreshed independently of capturing an image. FieldStation can save all images and metadata to four USB storage devices simultaneously for data redundancy.

FieldPrism software

FieldPrism is a simple-to-use Python program that can be run from the command line and customized with a configuration file. The source code, user manual, and FieldStation build guide are freely available at https://www.fieldprism.org and https://github.com/Gene-Weaver/FieldPrism (see Data Availability Statement). We also provide FieldSheets (six sizes), size-check templates, example naming hierarchy files, and example images at https://www.fieldprism.org/demo/FieldPrism_Kit.zip. The software can be installed on Windows, Linux, and OS X systems inside of a Python virtual environment. Full installation instructions are available at our GitHub repository. To process images with FieldPrism, adjust settings in the “FieldPrism.yaml” configuration file and then run the “FieldPrism.py” file. We outline the steps of the FieldPrism software below.

  • Step 1: Preprocessing and supported image formats

  • Before running FieldPrism, it may be useful to perform some preprocessing on the images. FieldPrism does not adjust color, brightness, or contrast, so if lighting conditions were poor at the time of collection, then batch-processing tools like Adobe Lightroom (Adobe, San Jose, California, USA) can be used to improve the quality of the images. Similarly, if the camera lens has a manufacturer-provided lens distortion correction profile, then it should be applied to the batch of images at this stage. All images should be exported as full-resolution JPG files. If the collection project is intended to emulate fusion or photo vouchers, or if color accuracy is important, then an archival-grade color correction card should also be included in the image, affixed to the clipboard (Funk et al., 2017; Gómez-Bellver et al., 2019).

  • Step 2: Identifying photogrammetric markers

  • After preprocessing images, FieldPrism needs to identify the photogrammetric markers. Prior to processing, FieldPrism will rotate all images into a portrait orientation, which is necessary to determine the orientation and skew of the FieldSheet in each image. FieldPrism will also check for corrupt image files and illegal characters in file names, replacing illegal characters with a dash or underscore.

    We trained a custom implementation of a YOLO-v5 object detection machine learning network (Jocher et al., 2022) to locate photogrammetric markers on the FieldSheet. We compiled 1119 images taken with six different cameras (Table 1) that contained examples of all five versions of our FieldSheet, indoor and outdoor locations, very dim to very bright lighting conditions, full QR code sheets, QR code flipbooks, and examples of ideal and poor imaging scenarios. To bolster the reliability and generalizability of our network, we used transfer learning to extend the capabilities of a YOLO-v5 network that was trained on 6394 fully annotated herbarium specimen images from an unpublished data set. This data set is composed of angiosperm images sampled from all contributing herbaria in the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/); the images were annotated to identify archival components including rulers, labels, maps, photographs, attached items, barcodes, color correction cards, and weights. The full list of sampled herbaria data sets is available in Appendix 1. The FieldPrism YOLO-v5 variant locates QR codes, photogrammetric markers, and (optionally) text labels.

  • Step 3: Distortion correction

  • With bounding box predictions around each of the four photogrammetric markers, FieldPrism calculates centroids and determines the orientation of the FieldSheet within the image based on the relative marker locations. It then calculates the perspective transformation required to deskew the FieldSheet and apply a geometric transformation to the entire image, creating a distortion-corrected image. If all four photogrammetric markers cannot be identified, then the image cannot be deskewed, but all identified photogrammetric markers are processed in step 4 to attempt to calculate the pixel-to-metric conversion ratio.

  • Step 4: Pixel-to-metric conversion

  • Using the distortion-corrected image, FieldPrism re-identifies the four photogrammetric markers with the trained YOLO-v5 network. Within each predicted bounding box, FieldPrism progressively implements image binarization (the process of segmenting an image into zeros and ones given a color or intensity threshold) through a range of threshold values until the binary image matches a known, ideal binary representation of the photogrammetric markers. In the user manual, we outline a progressive process that attempts metrological conversion despite poor image quality (e.g., uneven lighting).

  • Step 5: Locate and decode QR codes

  • Using the trained YOLO-v5 network, FieldPrism places bounding boxes around all QR codes in both the distortion-corrected images and the images that failed the correction process. The QR codes contain metadata indicating the hierarchical level to which the specimen identifier component belongs, together defining the specimen identifier contained within the set of QR codes. Users can define error handling for indecipherable QR codes, but the default setting in FieldPrism reconstructs the specimen identifier with placeholder values like “Level-2” to fill in for the unrecoverable specimen identifier component. These placeholder values aid quality control and make it simple to correct the final names. If the project utilized an indeterminate hierarchical naming structure, at this stage specimen identifiers are converted to the final informative file name using the alias table. If multiple images contain the same set of identifiers, then FieldPrism can append either a number or the original filename to the end of the specimen identifier.

  • Step 6: Apply user-configurable processing options

  • Distortion-corrected images can be processed to meet different user requirements. Step 3 outputs an image that retains most of the content and similar dimensions to the unprocessed image (Figure 3C). Users can opt for a top-left justified image, which maintains some of the original background and the same pixel density as the unprocessed image (Figure 3D). Alternatively, users can specify image dimensions for the whole data set (e.g., 3000 × 4000 pixels). All images will be scaled and cropped, resulting in a set of uniform images, regardless of the original skew or distance from the camera (Figure 3E).


We validated the performance of our FieldSheets and QR codes by photographing a full sheet of QR codes placed in the center of an A3-sized FieldSheet starting at the intended working distance and increasing up to 6 m from the FieldSheet. The intended working distance varies by camera and lens combination but is the distance where the FieldSheet photogrammetric markers nearly fill the frame (Figure 3E). In a university conference room with controlled bright lighting, we taped the FieldSheet to a whiteboard and removed most distracting items. For this test, we used four cameras including a Canon R5 (45 megapixels; mirrorless camera; Canon, Tokyo, Japan), a Panasonic Lumix G9 (20.3 megapixels; mirrorless camera; Panasonic, Osaka, Japan), a Samsung Galaxy S9+ (12 megapixels; smartphone; Panasonic, Suwon, South Korea), and the OAK-1 (12 megapixels; machine vision camera), which is the camera used by our FieldStation system. For this test, all cameras used built-in autofocus. We also printed the QR code onto three different kinds of paper using the same laser toner printer: standard copier paper, Avery 5353 Copier Shipping Labels, and Avery 5526 Waterproof Film Shipping Labels. Our performance metrics were the proportion of successfully decoded QR codes, converted photogrammetric markers, and distortion corrections at each distance.

QR code validation

FieldPrism decoded 99.5% of QR codes in images taken at the intended working distance (less than 1 m from the subject), indicating that a variety of possible equipment configurations will produce good results (Appendix S2A). At 1 m, the high-resolution Canon R5 and Lumix G9 each achieved a similar success rate of 99.1%. However, this success rate drops sharply to only 15.6% at 1 m for the pair of 12-megapixel cameras. Beyond 1 m, the high-resolution cameras achieve some success, but the low-resolution cameras fail. Image resolution and distance from the subject strongly impact the success of QR code decoding.

FieldSheet validation for distortion correction

All camera and paper combinations performed exceptionally well at the intended working distance, with successful distortion correction at 1 m from the FieldSheet (Appendix S2B). Beyond 1 m, the two high-resolution cameras performed best, with correction achieved up to 4 m from the FieldSheet. None of the cameras could correct images beyond 4 m.

Metric validation

With the same data set, we also calculated the proportional error between the 10-cm scale bar printed on the FieldSheet and the 10-cm scale bar predicted by FieldPrism. For this test, we used the default pixel-to-metric conversion settings in the FieldPrism configuration file, which requires all four photogrammetric markers to be located and successfully yield a conversion ratio. Four ratios are averaged per image to produce the predicted 10-cm scale bar. Image distortion correction was successful for the images in this set, so it did not include any images taken more than 4 m away from the FieldSheet (Appendix S2C). Across all combinations of paper, distance, and cameras, we found that on average FieldPrism underestimated the true conversion ratio by 0.97 mm per 10 cm or an error rate of less than 1% (Table 3).

Table 3. Validating the accuracy of pixel-to-metric conversion. The results show that (1) images from all four cameras had an error of roughly 1 mm per 10 cm, (2) standard printer paper performed best with an error of only 0.5 mm per 10 cm, and (3) error decreased with distance. However, only the two high-resolution cameras contributed to the 2-m and 3-m calculations, partly explaining the lower variance.
Variable Error (mm) Standard deviation Sample size
Canon R5 (45 MP) −0.96 0.77 22
Lumix G9 (20 MP) −0.99 0.44 12
OAK-1 (12 MP) −1.02 1.68 13
Samsung Galaxy S9+ (12 MP) −0.91 0.51 12
Paper type
Print paper −0.58 0.34 20
Package label −1.30 1.30 19
Waterproof package label −1.06 0.76 20
Distance from FieldSheet
Minimal −1.56 1.30 22
1 m −0.70 0.50 21
2 m −0.69 0.13 10
3 m −0.52 0.18 6
Overall −0.97 0.93 59
  • Note: MP = megapixel.

Field testing

With the help of early testers at the Morton Arboretum (Lisle, Illinois, USA), we validated a prototype implementation of our FieldSheets and QR code system by collecting images of Quercus havardii Rydb. in Texas (Figure 3, row 2; Appendix S3). The prototype system used a single QR code per specimen and had grayscale photogrammetric markers. Collected specimens were flattened on the FieldSheet using a thin piece of transparent acrylic. This test led to several improvements, including the development of the QR code flipbook, because managing individual barcodes was too cumbersome and limited flexibility while collecting specimens. The grayscale markers also made pixel-to-metric conversion inconsistent and dependent on lighting conditions, prompting the shift to solid black markers. Despite these issues, the FieldPrism software corrected image distortion for 205 of the 208 imaged specimens. The three failures were due to intense sun glare directly over the photogrammetric markers, caused by the transparent acrylic (Appendix S3C). FieldPrism identified the locations of 100% of the barcodes and successfully decoded 70.2%. The decoding failures were caused by inconsistent lighting (e.g., half of the QR code was in full sun while half was in shadow) or three-dimensional distortion of the QR code (e.g., curled paper). For the best results, we recommend printing all material on waterproof paper and attaching QR codes to a notebook to minimize three-dimensional distortion. Controlling lighting can be difficult, but FieldPrism can compensate for poor lighting so long as shadows do not bisect the QR codes.

GPS accuracy

To validate the accuracy of the FieldStation GPS module and active antenna (Appendix S1), we compared its accuracy to a consumer-grade handheld GPS receiver, the Garmin GPSMAP 66sr (Garmin, Olathe, Kansas, USA), in a lightly wooded, hilly park near the University of Michigan campus. Two people walked 1 m apart on a path through the park for 20 min. FieldStation recorded GPS coordinates every 20 s, and Garmin recorded every 15 s while in motion. Qualitatively, we observed virtually no drift with the Garmin points, but we did observe drifting points of up to 4 m off the path for the FieldStation GPS. Then, we placed both GPS devices in the same location and recorded coordinates concurrently for 15 min at the previously mentioned rates. While stationary, the FieldStation GPS module achieved greater precision than Garmin. The minimum bounding polygon that contained all Garmin coordinates (n = 67) was 6.94 m², while the FieldStation bounding polygon (n = 38) was 1.76 m². The circular error probability (CEP; the radius of a circle within which 50% of the coordinates are expected to fall) was 0.70 m for the Garmin and 0.57 m for the FieldStation. The root mean square accuracy at the 95% confidence level (RMS; a measure of the average deviation between a set of coordinates and their centroid for both horizontal and vertical distances) was 1.70 m for the Garmin and 0.97 m for the FieldStation. The FieldStation active external antenna seemed to aid stationary accuracy, while Garmin's GPS correction algorithms improved performance while in motion. For additional GPS performance and device comparisons, please refer to U.S. Forest Service Missoula Technology and Development Center (MTDC) test reports (USDA Forest Service, 2023).

FieldStation battery life

We tested battery life by instructing different configurations of the FieldStation to capture GPS data and three photos per minute until the battery drained. Battery testing revealed that the OAK camera and the Raspberry Pi should be powered with two separate battery banks. By powering the Raspberry Pi with a 26,000 mAh battery and the OAK-1 camera with a 20,000 mAh battery, we achieved between 17 and 20 h of continuous run time, yielding more than 3000 photos.


To enhance the quality and usefulness of field reference images and increase their potential for downstream applications, the FieldPrism workflow generates curated snapshot vouchers. FieldPrism standardizes images, emulating traditional herbarium specimens, with the goal of enabling accurate morphometric and phenological measurements using other tools like ImageJ or LeafMachine2 (Rueden et al., 2017; Weaver and Smith 2023). FieldPrism methods can greatly improve the utility of field images with minimal effort (Figure 1). For example, the immense trove of observations submitted to the iNaturalist portal is a valuable source of biological data, but citizen science initiatives like iNaturalist could greatly enhance the utility of the collected photographs (Heberling and Isaac, 2018). While there are more than 7 million plant observations in iNaturalist, a scale bar rarely makes an appearance (Seltzer, 2019). Our FieldSheet is a powerful enhancement tool for citizen science initiatives like iNaturalist. The inclusion of a photogrammetric scale bar extends the utility of crowdsourced images beyond the scoring of the presence or absence of traits and could produce a valuable data set of accurately measured traits at an unprecedented scale. FieldPrism is a valuable resource for organizing and standardizing field images, an important first step in collecting data and measurements from images.


W.N.W. planned and designed the project, wrote the software, built the FieldStation hardware, and wrote the initial manuscript. S.A.S. contributed to project planning and design, imaging, and testing. Both authors contributed to revising and editing the text and approved the final version of the manuscript.


The authors thank Dr. Charles Cannon, Samantha Panock, and Claire Henley at the University of Chicago Morton Arboretum for testing and providing feedback on an early prototype of our FieldSheets, QR codes, and FieldStation. The authors also thank Megan Kastelen and Sophie Farr (University of Michigan) for assisting with labeling machine learning training data and the members of the Smith Lab (University of Michigan) for providing insightful feedback and encouragement. Funding was provided by the National Science Foundation (DEB 2217116), Michigan Institute for Data Science, and the Michigan Institute for Computational Discovery and Engineering.


    A list of the data sets that provided training images for our machine learning algorithm. This unpublished data set of annotated herbarium specimen vouchers sampled up to 50 images per herbarium to capture a diversity of specimen preparation techniques and image quality, yielding 6394 herbarium vouchers to supplement our 1119 FieldPrism training images. The data set is available at https://doi.org/10.15468/dl.bh9dem.

    Herbarium data set name Data set DOI
    University of Tennessee Vascular Herbarium 10.15468/ok8qvz
    Marie-Victorin Herbarium 10.5886/rzav8bu2
    Herbarium of Yugra State University 10.15468/z8mpt5
    San Diego Natural History Museum 10.15468/lneqwn
    The Vascular Plant Collection at the Botanische Staatssammlung München 10.15468/vgr4kl
    University of Florida Herbarium 10.15468/v5wjn7
    Steiermarkisches Landesmuseum Joanneum 10.15468/dmdck6
    Canadian Museum of Nature Herbarium 10.15468/kowta4
    Species recordings from the Danish National portal Arter.dk 10.15468/q3yy4u
    Texas Tech University 10.15468/uyakmh
    Forest Herbarium Ibadan 10.15468/uhnd5n
    University of Graz Institute of Plant Sciences 10.15468/axtkuz
    Fresno State Herbarium 10.15468/puyrj8
    MAG Herbarium 10.15468/ahqbdc
    HVASF herbarium 10.15468/kz6y6z
    UiT Tromsa Museum 10.15468/14epds
    Terre d'huiles 10.15468/wr1vhd
    Charles University Prague 10.15468/8xrt7r
    University of Michigan Herbarium 10.15468/nl8bvi
    Rio de Janeiro Botanical Garden Herbarium Collection 10.15468/bbsqoa
    CRSN herbarim from Kahuzi-Biega National Park 10.15468/bhvwem
    University of Lethbridge Herbarium 10.5886/wrt547hq
    IAN herbarium 10.15468/cv2dmt
    San Diego State University Herbarium 10.15468/8sx2ag
    MUFAL herbarium 10.15468/viuv6v
    Dataflos 10.15468/dcc6j8
    Nitraria komarovii 10.15468/jp2qco
    UTEP Plants (Arctos) 10.15468/yhb6ky
    TRH, NTNU University Museum 10.15468/zrlqok
    Flora of the Korean Peninsula 10.15468/0vcvsq
    Botanical Museum Berlin-Dahlem 10.15468/ed17cn
    University of Tartu Natural History Museum 10.15468/5hqb2z
    TKM Herbarium 10.15468/sfxvrv
    Museum d'Histoire Naturelle of Aix-en-Provence 10.15468/fqykeb
    Institut Scientifique Mohamed V University 10.15468/48pwft
    Rhoen and Vogelsberg 10.15468/hbhfi3
    George Mason University 10.15468/t8ar55
    The Exsiccatal Series 10.15468/qxmief
    CSBG SB RAS Digital Herbarium 10.15468/6f3ybc
    Instituto do Meio Ambiente do Estado de Alagoas 10.15468/mu8w57
    Brown University 10.15468/kpsj8r
    Forest Herbarium Ibadan Nigeria 10.15468/rhbyxz
    Cal State LA Herbarium 10.15468/36qz6p
    E. C. Smith Herbarium 10.15468/zc4csq
    Clemson University Herbarium 10.15468/srjd22
    California Botanic Garden Herbarium 10.15468/0yosx9
    University of California, Los Angeles Herbarium 10.15468/33k42a
    Cal Poly State University 10.15468/mypdjd
    Harvard University Herbarium 10.15468/29fhdy
    Field Museum of Natural History 10.15468/pyjtoc
    Carnegie Museum of Natural History Herbarium 10.15468/d51v1f
    Botanische Staatssammlung München 10.15468/ni5yho
    Royal Ontario Museum Green Plant Herbarium 10.5886/g7j6gct1
    CEN herbarium 10.15468/wasmx9
    NMNH 10.15468/hnhrg3
    NEON Biorepository 10.15468/ggrfcb
    San Jose State University 10.15468/t3a60p
    Sagehen Herbarium 10.15468/fl8uov
    University of New Mexico Herbarium 10.15468/dlvoyt
    Artportalen (Swedish Species Observation System) 10.15468/kllkyl
    Royal Botanic Gardens, Kew 10.15468/ly60bx
    CAS Botany 10.15468/7gudyo
    Norwegian Species Observation Service 10.15468/zjbzel
    Universidade Federal do Ceara 10.15468/s8xuen
    National Academy of Sciences of Republic of Armenia 10.15468/xn64eb
    Humboldt State University 10.15468/qguk7r
    Tallinn Botanic Garden 10.15468/hfs8d4
    Naturalis Biodiversity Center 10.15468/ib5ypt
    Brauckmann at the Botanische Staatssammlung München 10.15468/onfqgb
    Komarov Botanical Institute 10.15468/udzn9d
    Genus Medicago in CSBG Herbarium 10.15468/jvrxeh
    Qarshi Botanical Garden 10.15468/pjxa84
    Rio de Janeiro Botanical Garden Herbarium 10.15468/7ep9i2
    Kenai National Wildlife Refuge (Arctos) 10.15468/ycpd7y
    Tropicos Specimen Data 10.15468/hja69f
    Queensland Museum 10.15468/lotsye
    Herbarium Horti Botanici Pisani 10.15468/soyil7
    University of Sargodha Herbarium 10.15468/n4k5s9
    Rutgers University 10.15468/1n787c
    CSBG SB RAS 10.15468/67ouin
    CRI Herbarium 10.15468/vvctbg
    Jardins botaniques and Conservatoire Botanique of Nancy 10.15468/g1zohr
    SVER Herbarium 10.15468/xwzszg
    Field Museum of Natural History 10.15468/4nodxs
    Asociacian Jardan Botanico La Laguna 10.15468/gfwydn
    National Museum of Natural History Luxembourg 10.15468/s2iu7d
    Botanical Collections of the Abo Akademi 10.15468/mpsjrk
    Natural History Museum, Vienna 10.15468/5sl7sh
    KUZ Herbarium 10.15468/4ru3f6
    Herbarium Hamburgense 10.15468/31iaih
    Quaid-i-Azam University Herbarium 10.15468/bp6jy3
    Universidade Federal de Parana 10.15468/fpf5j6
    Dr. Sultan Ahmad Herbarium 10.15468/xaju4z
    Ministerio del Medio Ambiente de Chile 10.15468/ezyu58
    JOI Herbarium 10.15468/pf6pv2
    NSW AVH data 10.15468/jf3yae
    Arizona State University 10.15468/a2o8vy
    IICT Herbario 10.15468/iinlqm
    Moscow University Herbarium 10.15468/cpnhcc
    The New York Botanical Garden Herbarium 10.15468/6e8nje
    San Francisco State University 10.15468/6zdzvc
    University of South Florida Herbarium 10.15468/mdnmzb
    Towson University 10.15468/podgza
    SAMES herbarium 10.15468/l0hdtn
    Institut Botanic de Barcelona 10.15468/pff0t6
    Universidade Federal do Oeste do Para 10.15468/ztzkde
    South-Siberian Botanical Garden 10.15468/y6xmme
    Universidad del Valle de Guatemala 10.15468/u339qt
    Universidade Federal de Sergipe 10.15468/9xujh5
    California State University, Long Beach 10.15468/3y25yl
    University of Tennessee Fungal Herbarium 10.15468/da30il
    Botanische Staatssammlung München 10.15468/sookye
    Herbarium Willing at Herbarium Berolinense, Berlin 10.15468/abcz8i
    CRSN Herbarium 10.15468/ra9vp0
    University of California, Riverside 10.15468/ai1kou
    South Australian Museum Australia 10.15468/wz4rrh
    Central Siberian Botanical Garden Herbarium 10.15468/5wcerp
    Universidade Tecnolagica Federal do Parana 10.15468/4b74v2
    Newhaven Sanctuary Observations 10.15468/mwgsdh
    Georgian Academy of Sciences 10.15468/6tbhmd
    Arkansas Natural Heritage Commission Herbarium 10.15468/v94jsu
    Royal Botanic Garden Edinburgh Herbarium 10.15468/ypoair
    University of Jena, Herbarium Haussknecht 10.15468/8arhjc
    University of North Carolina at Chapel Hill 10.15468/63vxjd
    CBNA 10.15468/oc5zh7
    Herbier Louis-Marie 10.5886/3p8ltbg7
    Real Jardan Botanico 10.15468/mug7kr
    Estonian University of Life Sciences 10.15468/m3x9uu
    Herbarium Generale 10.15468/dg4cb4
    UC Davis Herbarium 10.15468/on4axg
    Herbier du Quabec 10.5886/jd11sg3p
    BRI AVH 10.15468/jsffsa
    Eastern Kentucky University 10.15468/fy8dsi
    Sociata des Sciences Naturelles et Mathamatiques de Cherbourg 10.15468/lmznjw
    Natural History Museum 10.5519/0002965
    California State University, Northridge 10.15468/nrcdx7
    Herbarium Fennoscandicum 10.15468/ekpyfw
    The New York Botanical Garden 10.15468/5y84ye
    CHAS Botany Collection (Arctos) 10.15468/ji4vbl
    TUL Herbarium 10.15468/ca08cm
    Herbarium of Numto Nature Park 10.15468/g4gcrg
    Staten Island Museum 10.15468/ctqpb5
    University of Tartu Natural History Museum and Botanical Garden 10.15468/d59dmk
    TULGU Herbarium 10.15468/5nret6
    University of Kentucky 10.15468/fi4vfu
    Coleccian Herbario Federico Medem Bogota 10.15472/ighftu
    Herbario Joao de Carvalho e Vasconcellos 10.15468/olfpjv
    Masaryk University 10.15468/soarvd
    B.M. Kozo-Polyansky VSU 10.15468/xyqng3
    Bell Museum 10.15468/bihrxd
    University of Gothenburg 10.15468/asgd85
    NHMD Vascular Plants Collection 10.15468/4zygkn
    Naturhistorisches Museum Mainz 10.15468/l0wmu8
    Universidade Federal do Rio Grande do Norte 10.15468/gtxawd
    Botanische Staatssammlung München 10.15468/zinzhd
    Capture of Primary Biodiversity Data for West African Plants 10.15468/9czcig
    Flora of Sumatra: ANDA Herbarium 10.15468/ue7xyn
    Universidade Federal da Bahia 10.15468/tbtrr3
    Universidade Federal de Goias 10.15468/fw6hdt
    Black Hills State University Herbarium 10.15468/ptcrqx
    Desert Botanical Garden Herbarium 10.15468/abe1lg
    NSW South Coast 10.15468/px2xfi
    MHA Herbarium 10.15468/827lk2
    Musaum National da Histoire Naturelle, Paris 10.15468/kw8pex
    Flora Sumatra: (ANDA)-Part 2 10.15468/55evew
    Nova Scotia Museum of Natural History 10.15468/tl3cde
    Universita de Montpellier 10.15468/gyvkrn
    Meise Botanic Garden Herbarium 10.15468/wrthhx
    Melu AVH 10.15468/2yyu7i
    Fairchild Tropical Botanic Garden 10.15468/hdpruf
    SVER Herbarium 10.15468/5npjcc
    R. L. McGregor Herbarium 10.15468/htptzr
    NEON Biorepository 10.15468/bmmg36
    Institute of Biological Problems of the North, Far East Branch RAS 10.15468/ms9q2t
    Universidade Federal do Esparito Santo 10.15468/kasze8
    Central Siberian Botanical Garden 10.15468/qdfdqq
    Centro de Pesquisas do Cacau 10.15468/vg8rjh
    Naturhistoriska Riksmuseet 10.15468/jbcsfu
    University of Vienna, Institute for Botany 10.15468/tnj8wm
    California State University San Bernardino 10.15468/t885ps
    Estonian Museum of Natural History 10.15468/bquqpv
    Flora of the Korean Peninsula 10.15468/fyxnsd
    University of Balochistan Herbarium 10.15468/qrau0v
    Royal Botanic Gardens, Kew 10.15468/rvrsru
    The James C. Parks Herbarium at Millersville University 10.15468/qdatdf
    Earth Sciences Collection (Arctos) 10.15468/4n2ev3
    Brown University Herbarium 10.15468/njgg1a
    Botanische Staatssammlung München 10.15468/zdcclb
    Bush Heritage - Carnarvon Station Reserve 10.15468/q0dhpr
    Herbarium Berolinense, Berlin 10.15468/dlwwhz
    University of Manitoba Herbarium 10.5886/2fva5p4r
    University of Colorado Museum of Natural History 10.15468/wyofjv
    Museu Paraense Emalio Goeldi 10.15468/rdq4nx
    Herbiers Universitaires de Clermont-Ferrand 10.15468/9axq0b
    Turku University 10.15468/nsyt4y
    Vascular Plant Herbarium, Oslo 10.15468/wtlymk
    Kathryn Kalmbach Herbarium 10.15468/axrelr
    Museu Botanico Municipal Curitiba 10.15468/v52pmc
    North Carolina State University 10.15468/9ufthy
    Botanische Staatssammlung München 10.15468/lqetda
    Universidade Estadual do Norte Fluminense 10.15468/qsaagd
    CRSN herbarium 10.15468/exh7vo
    Herbario Museo de La Salle Bogota 10.15472/ppzpea
    Universidade Regional de Blumenau 10.15468/vse5f3
    Appalachian State University 10.15468/ivsxey
    Clamence Lortet herbarium 10.15468/e64fbk
    Yale Peabody Museum 10.15468/hrztgn
    SANT Herbarium 10.15468/dgbpla
    Cape Breton University Collection 10.15468/7dtqgn
    UAM Herbarium (Arctos) 10.15468/iawody
    Universita Lyon 10.15468/7m584w
    Harvard University Herbaria 10.15468/o3pvnh
    CSBG 10.15468/c1y9q2
    Colorado Plateau Museum of Arthropod Biodiversity 10.15468/du1hci
    Coleccian de plantas vasculares del herbario de la Universitat de Valancia 10.15468/xmki52
    NCSM Herbarium Collection 10.36102/dwc.12
    Botanischer Garten und Botanisches Museum Berlin-Dahlem Herbarium 10.15468/tgwryf
    University of Vermont 10.15468/crnsua
    Universidade de Sao Paulo 10.15468/nt6dng
    Komi Republic 10.15468/336sdv
    Universidade Estadual de Feira de Santana 10.15468/gsy3jn
    Natural History Museum Rotterdam 10.15468/kwqaay
    University of South Carolina 10.15468/fmj4at
    Northern Arizona University 10.15468/b7tfpa
    CSBG SB RAS Digital Herbarium 10.15468/7anvyu
    CSBG SB RAS Herbarium Collections 10.15468/sunx5n
    McGill University Herbarium 10.5886/srzbj7
    Intermountain Herbarium 10.15468/t43wjj
    Universidade Federal de Uberlandia 10.15468/cshs8n
    Musaum National d'Histoire Naturelle 10.15468/nc6rxy
    Universidade Federal do Rio Grande Do Sul 10.15468/suhqjx
    Lord Fairfax Community College Herbarium 10.15468/c2gj2t
    University of California Santa Barbara Herbarium 10.15468/qpxmw0
    Vascular Plant Herbarium, UiB 10.15468/ofn0lf
    Botanische Staatssammlung München 10.15468/dixlft
    Field Museum of Natural History 10.15468/nxnqzf
    University of Cincinnati 10.15468/bhgpmq
    Rutgers University 10.15468/hhnd4h
    Universidade Federal Rural do Rio de Janeiro 10.15468/0svt7m
    NEON Biorepository 10.15468/bmmdp5
    Santa Barbara Botanic Garden 10.15468/adb2bb
    Herbarium GAT 10.15468/hiiw6b
    Auckland Museum Botany Collection 10.15468/mnjkvv
    University of California Santa Cruz 10.15468/uavt0t
    CSBG SB RAS 10.15468/be6owh
    Central Michigan University Herbarium 10.15468/iykbez
    Herbarium Senckenbergianum 10.15468/ucmdjy
    University of Hargeisa Herbarium 10.15468/qvbvdp
    Yale Peabody Museum 10.15468/0lkr3w
    Herbarium Generale 10.15468/83cb4a
    KULPOL Herbarium 10.15468/h9qfje
    University of Tennessee 10.15468/64w2b1
    Universidade Federal de Rondania 10.15468/5cjyj6
    University of Cincinnati 10.15468/xkca3p
    Herbarium of the University of Granada 10.15470/k97bjm
    Missouri Botanical Garden 10.15468/mmbcpb
    California State University Fullerton 10.15468/1uvzxh
    Universidade Estadual do Oeste do Parana 10.15468/eqp1dr
    Herbarium of Andalas University 10.15468/sncpxn
    Lajitietokeskus FinBIF 10.15468/4g56tp
    Chico State Herbarium 10.15468/ckxw7v
    Berea College 10.15468/hcwetj
    MEL AVH 10.15468/rhzrxw
    Western Carolina University Herbarium 10.15468/sk26v2
    Museu de Biologia Mello Leitao 10.15468/dmkg7b
    Plant Resources Center 10.15468/g85t8z
    Allan Herbarium 10.15468/x5ucvh
    University of Vermont 10.15468/zsgiog
    University of British Columbia Herbarium 10.5886/rtt57cc9
    CSBG SB RAS 10.15468/zw7jnn

    Open Research Badges

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    This article has earned Open Data and Open Materials badges. Data and materials are available at https://zenodo.org/badge/latestdoi/549185805 and https://github.com/Gene-Weaver/FieldPrism.


    The FieldPrism source code, example data sets, FieldSheets, FieldStation build guide, and user manual are available at https://github.com/Gene-Weaver/FieldPrism and https://www.FieldPrism.org. Video tutorials for using the FieldStation are available at https://www.FieldPrism.org.