Vuruputoor, V. S., D. Monyak, K. C. Fetter, C. Webster, A. Bhattarai, B. Shrestha, S. Zaman, et al. 2023. Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes. Applications in Plant Sciences 11(4): e11533.
Figure 4 in the published manuscript contained the following errors. Figure 4A and 4B were missing violin plots for MAKER, which should have been colored green. Figure 4C incorrectly displayed the ideal range of scores—the yellow bar should have spanned 85 to 100 instead of the range shown. Additionally, the color scheme was incorrect. The BRAKER runs should have been colored blue and the StringTie2 runs should have been red. The corrected Figure 4 is presented here with its original caption, which was correct.
We apologize for this error.
Vuruputoor, V. S, D. Monyak, K. C. Fetter, C. Webster, A. Bhattarai, B. Shrestha, S. Zaman等。2023。欢迎来到大叶子:改进非模式植物基因组注释的最佳实践。植物科学应用11(4):e11533。已发表稿件中的图4包含以下错误。图4A和4B缺少MAKER的小提琴情节，应该用绿色标注。图4C错误地显示了分数的理想范围——黄色条应该跨越85到100，而不是显示的范围。此外，配色方案也不正确。BRAKER的运行应该是蓝色的，StringTie2的运行应该是红色的。更正后的图4显示在这里，其原始标题是正确的。我们为这个错误道歉。
{"title":"Correction to Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes","authors":"","doi":"10.1002/aps3.11553","DOIUrl":"10.1002/aps3.11553","url":null,"abstract":"<p>Vuruputoor, V. S., D. Monyak, K. C. Fetter, C. Webster, A. Bhattarai, B. Shrestha, S. Zaman, et al. 2023. Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes. <i>Applications in Plant Sciences</i> 11(4): e11533.</p><p>Figure 4 in the published manuscript contained the following errors. Figure 4A and 4B were missing violin plots for MAKER, which should have been colored green. Figure 4C incorrectly displayed the ideal range of scores—the yellow bar should have spanned 85 to 100 instead of the range shown. Additionally, the color scheme was incorrect. The BRAKER runs should have been colored blue and the StringTie2 runs should have been red. The corrected Figure 4 is presented here with its original caption, which was correct.</p><p>We apologize for this error.</p>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 6","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135539842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christina Steinecke, Jeremiah Lee, Jannice Friedman
Premise
Variation in seed traits is common within and among populations of plant species and often has ecological and evolutionary implications. However, due to the time-consuming nature of manual seed measurements and the level of variability in imaging techniques, quantifying and interpreting the extent of seed variation can be challenging.
Methods
We developed a standardized high-throughput technique to measure seed number, as well as individual seed area and color, using a derived empirical scale to constrain area in Arabidopsis thaliana, Brassica rapa, and Mimulus guttatus. We develop a specific rational model using seed area measured at various spatial scales relative to the pixel count, observing the asymptotic value of the seed area as the modeled number of pixels approaches infinity.
Results
We found that our model has high reliability in estimating seed traits and efficiently processes large numbers of images, facilitating the quantification of seed traits in studies with large sample sizes.
Discussion
This technique facilitates consistency between imaging sessions and standardizes the measurement of seed traits. These novel advances allow researchers to directly and reliably measure seed traits, which will enable tests of the ecological and evolutionary causes of their variation.
{"title":"A standardized and efficient technique to estimate seed traits in plants with numerous small propagules","authors":"Christina Steinecke, Jeremiah Lee, Jannice Friedman","doi":"10.1002/aps3.11552","DOIUrl":"10.1002/aps3.11552","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Variation in seed traits is common within and among populations of plant species and often has ecological and evolutionary implications. However, due to the time-consuming nature of manual seed measurements and the level of variability in imaging techniques, quantifying and interpreting the extent of seed variation can be challenging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a standardized high-throughput technique to measure seed number, as well as individual seed area and color, using a derived empirical scale to constrain area in <i>Arabidopsis thaliana, Brassica rapa</i>, and <i>Mimulus guttatus</i>. We develop a specific rational model using seed area measured at various spatial scales relative to the pixel count, observing the asymptotic value of the seed area as the modeled number of pixels approaches infinity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We found that our model has high reliability in estimating seed traits and efficiently processes large numbers of images, facilitating the quantification of seed traits in studies with large sample sizes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>This technique facilitates consistency between imaging sessions and standardizes the measurement of seed traits. These novel advances allow researchers to directly and reliably measure seed traits, which will enable tests of the ecological and evolutionary causes of their variation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Because of the trade-off between water loss and carbon dioxide assimilation, the conductivity of the transpiration path in a leaf is an important limit on photosynthesis. Closely packed veins correspond to short paths and high assimilation rates while widely spaced veins are associated with higher resistance to flow and lower maximum photosynthetic rates. Vein length per area (VLA) has become the standard metric for comparing leaves with different vein densities; its measurement typically utilizes digital image processing with varying amounts of human input.
Methods and Results
Here, we propose three new ways of measuring vein density using image analysis that improve on currently available procedures: (1) areole area distributions, (2) a sizing transform, and (3) a distance map. Each alternative has distinct practical, statistical, and biological limitations and advantages. In particular, we advocate the log-transformed modal distance map of a vein mask as an estimator to replace VLA as a standard metric for vein density.
Conclusions
These methods, for which open-source code appropriate for high-throughput automation is provided, improve on VLA by producing determinate measures of vein density as distributions rather than point estimates. Combined with advances in image quality and computational efficiency, these methods should help clarify the physiological and evolutionary significance of vein density.
{"title":"How dense can you be? New automatic measures of vein density in angiosperm leaves","authors":"Walton A. Green, Juan M. Losada","doi":"10.1002/aps3.11551","DOIUrl":"10.1002/aps3.11551","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Because of the trade-off between water loss and carbon dioxide assimilation, the conductivity of the transpiration path in a leaf is an important limit on photosynthesis. Closely packed veins correspond to short paths and high assimilation rates while widely spaced veins are associated with higher resistance to flow and lower maximum photosynthetic rates. Vein length per area (VLA) has become the standard metric for comparing leaves with different vein densities; its measurement typically utilizes digital image processing with varying amounts of human input.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>Here, we propose three new ways of measuring vein density using image analysis that improve on currently available procedures: (1) areole area distributions, (2) a sizing transform, and (3) a distance map. Each alternative has distinct practical, statistical, and biological limitations and advantages. In particular, we advocate the log-transformed modal distance map of a vein mask as an estimator to replace VLA as a standard metric for vein density.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These methods, for which open-source code appropriate for high-throughput automation is provided, improve on VLA by producing determinate measures of vein density as distributions rather than point estimates. Combined with advances in image quality and computational efficiency, these methods should help clarify the physiological and evolutionary significance of vein density.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pamela S. Soltis, Luiza Teixeira-Costa, Pierre Bonnet, R. Gil Nelson
<p>New imaging technologies are dramatically transforming all of biology. From remote sensing of continents to computed tomography (CT) scanning of individual organisms or parts of organisms, novel views are emerging that span planetary to suborganismal scales. In plant biology, observations from satellites (e.g., Deneu et al., <span>2021</span>; Cavender-Bares et al., <span>2022</span>) and airborne instruments (e.g., Sun et al., <span>2021</span>) are providing new insight into the distribution of botanical diversity, species abundance, and ecosystem productivity and how these features are changing in response to human activity. At the same time, advances in X-ray technologies are revealing exquisite anatomical detail of both living and fossil plant structures (Brodersen and Roddy, <span>2016</span>). Innovations in imaging, largely enabled by the development of new sensors and analysis capabilities, are also capturing specific attributes of individual plants as well as their community context in the field.</p><p>In this special issue of <i>Applications in Plant Sciences</i> (<i>APPS</i>), we explore innovations in imaging and their contributions to plant biology. The 10 papers included in this collection span imaging of live plants in the field to chemical mapping of specific compounds. The authors emphasize sample preparation techniques, practical aspects of image capture, standardization of imaging techniques and resulting images, multiple forms of image analysis, and alternatives for image archival in public repositories. Moreover, the diversity of the imaging approaches and protocols presented in this collection can be applied to a broad range of research, teaching, and public outreach.</p><p>Two papers in this special issue note the lack of consistency in photographs of plants taken in the field. These photographs might serve as a virtual voucher of a rare species (when destructive sampling would be detrimental to the population) or as a source of plant traits for ecological or evolutionary research, but field photographs of plants are rarely standardized. Unlike other groups of organisms for which “standard views” have been developed, the vast diversity of plants in terms of both size and structure precludes many traditional approaches to standardization. These issues, as well as others, render currently available collections, such as those downloadable from iNaturalist (https://www.inaturalist.org/), less useful than they could be if images were captured, processed, and archived following specified standards. To standardize and improve the usefulness of field-captured images of plants, Weaver and Smith (<span>2023a</span>) report the development and implementation of FieldPrism, a system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers. They also developed FieldStation, a mobile imaging system that records images, GPS location, and other metadata on multiple storage devices. The combined u
{"title":"Advances in plant imaging across scales","authors":"Pamela S. Soltis, Luiza Teixeira-Costa, Pierre Bonnet, R. Gil Nelson","doi":"10.1002/aps3.11550","DOIUrl":"https://doi.org/10.1002/aps3.11550","url":null,"abstract":"<p>New imaging technologies are dramatically transforming all of biology. From remote sensing of continents to computed tomography (CT) scanning of individual organisms or parts of organisms, novel views are emerging that span planetary to suborganismal scales. In plant biology, observations from satellites (e.g., Deneu et al., <span>2021</span>; Cavender-Bares et al., <span>2022</span>) and airborne instruments (e.g., Sun et al., <span>2021</span>) are providing new insight into the distribution of botanical diversity, species abundance, and ecosystem productivity and how these features are changing in response to human activity. At the same time, advances in X-ray technologies are revealing exquisite anatomical detail of both living and fossil plant structures (Brodersen and Roddy, <span>2016</span>). Innovations in imaging, largely enabled by the development of new sensors and analysis capabilities, are also capturing specific attributes of individual plants as well as their community context in the field.</p><p>In this special issue of <i>Applications in Plant Sciences</i> (<i>APPS</i>), we explore innovations in imaging and their contributions to plant biology. The 10 papers included in this collection span imaging of live plants in the field to chemical mapping of specific compounds. The authors emphasize sample preparation techniques, practical aspects of image capture, standardization of imaging techniques and resulting images, multiple forms of image analysis, and alternatives for image archival in public repositories. Moreover, the diversity of the imaging approaches and protocols presented in this collection can be applied to a broad range of research, teaching, and public outreach.</p><p>Two papers in this special issue note the lack of consistency in photographs of plants taken in the field. These photographs might serve as a virtual voucher of a rare species (when destructive sampling would be detrimental to the population) or as a source of plant traits for ecological or evolutionary research, but field photographs of plants are rarely standardized. Unlike other groups of organisms for which “standard views” have been developed, the vast diversity of plants in terms of both size and structure precludes many traditional approaches to standardization. These issues, as well as others, render currently available collections, such as those downloadable from iNaturalist (https://www.inaturalist.org/), less useful than they could be if images were captured, processed, and archived following specified standards. To standardize and improve the usefulness of field-captured images of plants, Weaver and Smith (<span>2023a</span>) report the development and implementation of FieldPrism, a system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers. They also developed FieldStation, a mobile imaging system that records images, GPS location, and other metadata on multiple storage devices. The combined u","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71934122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katherine A. Wolcott, Edward L. Stanley, Osman A. Gutierrez, Stefan Wuchty, Barbara Ann Whitlock
Premise
Imaging technologies that capture three-dimensional (3D) variation in floral morphology at micro- and nano-resolutions are increasingly accessible. In herkogamous flowers, such as those of Theobroma cacao, structural barriers between anthers and stigmas represent bottlenecks that restrict pollinator size and access to reproductive organs. To study the unresolved pollination biology of cacao, we present a novel application of micro-computed tomography (micro-CT) using floral dimensions to quantify pollinator functional size limits.
Methods
We generated micro-CT data sets from field-collected flowers and museum specimens of potential pollinators. To compare floral variation, we used 3D Slicer to place landmarks on the surface models and performed a geometric morphometric (GMM) analysis using geomorph R. We identified the petal side door (an opening between the petal hoods and filament) as the main bottleneck for pollinator access. We compared its mean dimensions with proposed pollinators to identify viable candidates.
Results
We identified three levels of likelihood for putative pollinators based on the number of morphological (body) dimensions that fit through the petal side door. We also found floral reward microstructures whose presence and location were previously unclear.
Discussion
Using micro-CT and GMM to study the 3D pollination biology of cacao provides new evidence for predicting unknown pollinators. Incorporating geometry and floral rewards will strengthen plant–pollinator trait matching models for cacao and other species.
{"title":"3D pollination biology using micro-computed tomography and geometric morphometrics in Theobroma cacao","authors":"Katherine A. Wolcott, Edward L. Stanley, Osman A. Gutierrez, Stefan Wuchty, Barbara Ann Whitlock","doi":"10.1002/aps3.11549","DOIUrl":"https://doi.org/10.1002/aps3.11549","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Imaging technologies that capture three-dimensional (3D) variation in floral morphology at micro- and nano-resolutions are increasingly accessible. In herkogamous flowers, such as those of <i>Theobroma cacao</i>, structural barriers between anthers and stigmas represent bottlenecks that restrict pollinator size and access to reproductive organs. To study the unresolved pollination biology of cacao, we present a novel application of micro-computed tomography (micro-CT) using floral dimensions to quantify pollinator functional size limits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We generated micro-CT data sets from field-collected flowers and museum specimens of potential pollinators. To compare floral variation, we used 3D Slicer to place landmarks on the surface models and performed a geometric morphometric (GMM) analysis using geomorph R. We identified the petal side door (an opening between the petal hoods and filament) as the main bottleneck for pollinator access. We compared its mean dimensions with proposed pollinators to identify viable candidates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We identified three levels of likelihood for putative pollinators based on the number of morphological (body) dimensions that fit through the petal side door. We also found floral reward microstructures whose presence and location were previously unclear.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Using micro-CT and GMM to study the 3D pollination biology of cacao provides new evidence for predicting unknown pollinators. Incorporating geometry and floral rewards will strengthen plant–pollinator trait matching models for cacao and other species.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71982944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.
Methods
LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.
Results
LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.
Discussion
LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.
{"title":"From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2","authors":"William N. Weaver, Stephen A. Smith","doi":"10.1002/aps3.11548","DOIUrl":"10.1002/aps3.11548","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71432179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting.
Methods and Results
We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.
Conclusions
The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.
{"title":"Rapid imaging in the field followed by photogrammetry digitally captures the otherwise lost dimensions of plant specimens","authors":"Nicole James, Alex Adkinson, Austin Mast","doi":"10.1002/aps3.11547","DOIUrl":"10.1002/aps3.11547","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yvonne Luong, Ariel Gasca-Herrera, Tracy M. Misiewicz, Benjamin E. Carter
Abstract Premise There are relatively few studies of flower color at landscape scales that can address the relative importance of competing mechanisms (e.g., biotic: pollinators; abiotic: ultraviolet radiation, drought stress) at landscape scales. Methods We developed an R shiny pipeline to sample color from images that were automatically downloaded using query results from a search using iNaturalist or the Global Biodiversity Information Facility (GBIF). The pipeline was used to sample ca. 4800 North American wallflower (Erysimum, Brassicaceae) images from iNaturalist. We tested whether flower color was distributed non‐randomly across the landscape and whether spatial patterns were correlated with climate. We also used images including ColorCheckers to compare analyses of raw images to color‐calibrated images. Results Flower color was strongly non‐randomly distributed spatially, but did not correlate strongly with climate, with most of the variation explained instead by spatial autocorrelation. However, finer‐scale patterns including local correlations between elevation and color were observed. Analyses using color‐calibrated and raw images revealed similar results. Discussion This pipeline provides users the ability to rapidly capture color data from iNaturalist images and can be a useful tool in detecting spatial or temporal changes in color using citizen science data.
{"title":"A pipeline for the rapid collection of color data from photographs","authors":"Yvonne Luong, Ariel Gasca-Herrera, Tracy M. Misiewicz, Benjamin E. Carter","doi":"10.1002/aps3.11546","DOIUrl":"10.1002/aps3.11546","url":null,"abstract":"Abstract Premise There are relatively few studies of flower color at landscape scales that can address the relative importance of competing mechanisms (e.g., biotic: pollinators; abiotic: ultraviolet radiation, drought stress) at landscape scales. Methods We developed an R shiny pipeline to sample color from images that were automatically downloaded using query results from a search using iNaturalist or the Global Biodiversity Information Facility (GBIF). The pipeline was used to sample ca. 4800 North American wallflower (Erysimum, Brassicaceae) images from iNaturalist. We tested whether flower color was distributed non‐randomly across the landscape and whether spatial patterns were correlated with climate. We also used images including ColorCheckers to compare analyses of raw images to color‐calibrated images. Results Flower color was strongly non‐randomly distributed spatially, but did not correlate strongly with climate, with most of the variation explained instead by spatial autocorrelation. However, finer‐scale patterns including local correlations between elevation and color were observed. Analyses using color‐calibrated and raw images revealed similar results. Discussion This pipeline provides users the ability to rapidly capture color data from iNaturalist images and can be a useful tool in detecting spatial or temporal changes in color using citizen science data.","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
Conclusions
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.
{"title":"FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes","authors":"William N. Weaver, Stephen A. Smith","doi":"10.1002/aps3.11545","DOIUrl":"10.1002/aps3.11545","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Phillip C. Klahs, Elizabeth K. McMurchie, Jordan J. Nikkel, Lynn G. Clark
Abstract Premise Current methods for maceration of plant tissue use hazardous chemicals. The new method described here improves the safety of dissection and maceration of soft plant tissues for microscopic imaging by using the harmless enzyme pectinase. Methods and Results Leaf material from a variety of land plants was obtained from living plants and dried herbarium specimens. Concentrations of aqueous pectinase and soaking schedules were optimized, and tissues were manually dissected while submerged in fresh solution following a soaking period. Most leaves required 2–4 h of soaking; however, delicate leaves could be macerated after 30 min while tougher leaves required 12 h to 3 days of soaking. Staining techniques can also be used with this method, and permanent or semi‐permanent slides can be prepared. The epidermis, vascular tissue, and individual cells were imaged at magnifications of 10× to 400×. Only basic safety precautions were needed. Conclusions This pectinase method is a cost‐effective and safe way to obtain images of epidermal peels, separated tissues, or isolated cells from a wide range of plant taxa.
{"title":"A maceration technique for soft plant tissue without hazardous chemicals","authors":"Phillip C. Klahs, Elizabeth K. McMurchie, Jordan J. Nikkel, Lynn G. Clark","doi":"10.1002/aps3.11543","DOIUrl":"10.1002/aps3.11543","url":null,"abstract":"Abstract Premise Current methods for maceration of plant tissue use hazardous chemicals. The new method described here improves the safety of dissection and maceration of soft plant tissues for microscopic imaging by using the harmless enzyme pectinase. Methods and Results Leaf material from a variety of land plants was obtained from living plants and dried herbarium specimens. Concentrations of aqueous pectinase and soaking schedules were optimized, and tissues were manually dissected while submerged in fresh solution following a soaking period. Most leaves required 2–4 h of soaking; however, delicate leaves could be macerated after 30 min while tougher leaves required 12 h to 3 days of soaking. Staining techniques can also be used with this method, and permanent or semi‐permanent slides can be prepared. The epidermis, vascular tissue, and individual cells were imaged at magnifications of 10× to 400×. Only basic safety precautions were needed. Conclusions This pectinase method is a cost‐effective and safe way to obtain images of epidermal peels, separated tissues, or isolated cells from a wide range of plant taxa.","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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