StomaAI: an efficient and user‐friendly tool for measurement of stomatal pores and density using deep computer vision
Summary Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment,...
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| Veröffentlicht in: | The New phytologist 2023-04, Vol.238 (2), p.904-915 |
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| container_title | The New phytologist |
| container_volume | 238 |
| creator | Sai, Na Bockman, James Paul Chen, Hao Watson‐Haigh, Nathan Xu, Bo Feng, Xueying Piechatzek, Adriane Shen, Chunhua Gilliham, Matthew |
| description | Summary
Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment, and can represent a significant bottleneck to research pipelines.
To alleviate this, we introduce StomaAI (SAI): a reliable, user‐friendly and adaptable tool for stomatal pore and density measurements via the application of deep computer vision, which has been initially calibrated and deployed for the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass).
SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets.
SAI boosts the number of images that can be evaluated in a fraction of the time, so can obtain a more accurate representation of stomatal traits than is routine through manual measurement. An online demonstration of SAI is hosted at https://sai.aiml.team, and the full local application is publicly available for free on GitHub through https://github.com/xdynames/sai‐app. |
| doi_str_mv | 10.1111/nph.18765 |
| format | Article |
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Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment, and can represent a significant bottleneck to research pipelines.
To alleviate this, we introduce StomaAI (SAI): a reliable, user‐friendly and adaptable tool for stomatal pore and density measurements via the application of deep computer vision, which has been initially calibrated and deployed for the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass).
SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets.
SAI boosts the number of images that can be evaluated in a fraction of the time, so can obtain a more accurate representation of stomatal traits than is routine through manual measurement. An online demonstration of SAI is hosted at https://sai.aiml.team, and the full local application is publicly available for free on GitHub through https://github.com/xdynames/sai‐app.</description><identifier>ISSN: 0028-646X</identifier><identifier>EISSN: 1469-8137</identifier><identifier>DOI: 10.1111/nph.18765</identifier><identifier>PMID: 36683442</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>applied deep learning ; Arabidopsis ; Cereal crops ; Computer vision ; Computers ; convolutional neural network ; Density ; Humans ; Inspection ; Measurement ; Microscopy ; Phenotype ; phenotyping ; Plant physiology ; Plant Stomata - physiology ; Stomata</subject><ispartof>The New phytologist, 2023-04, Vol.238 (2), p.904-915</ispartof><rights>2023 The Authors. © 2023 New Phytologist Foundation</rights><rights>2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3885-85ecd84be345e049e89cc6e9e93f916d4140261a8fd96e7429e3f93cc516a4413</citedby><cites>FETCH-LOGICAL-c3885-85ecd84be345e049e89cc6e9e93f916d4140261a8fd96e7429e3f93cc516a4413</cites><orcidid>0000-0002-2840-2533 ; 0000-0003-0666-3078 ; 0000-0002-9573-6082 ; 0000-0002-7935-6151 ; 0000-0002-7958-5771 ; 0000-0002-9482-6115 ; 0000-0003-4417-614X ; 0000-0002-8648-8718 ; 0000-0002-7583-2384</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fnph.18765$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fnph.18765$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,1427,27901,27902,45550,45551,46384,46808</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36683442$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sai, Na</creatorcontrib><creatorcontrib>Bockman, James Paul</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><creatorcontrib>Watson‐Haigh, Nathan</creatorcontrib><creatorcontrib>Xu, Bo</creatorcontrib><creatorcontrib>Feng, Xueying</creatorcontrib><creatorcontrib>Piechatzek, Adriane</creatorcontrib><creatorcontrib>Shen, Chunhua</creatorcontrib><creatorcontrib>Gilliham, Matthew</creatorcontrib><title>StomaAI: an efficient and user‐friendly tool for measurement of stomatal pores and density using deep computer vision</title><title>The New phytologist</title><addtitle>New Phytol</addtitle><description>Summary
Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment, and can represent a significant bottleneck to research pipelines.
To alleviate this, we introduce StomaAI (SAI): a reliable, user‐friendly and adaptable tool for stomatal pore and density measurements via the application of deep computer vision, which has been initially calibrated and deployed for the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass).
SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets.
SAI boosts the number of images that can be evaluated in a fraction of the time, so can obtain a more accurate representation of stomatal traits than is routine through manual measurement. An online demonstration of SAI is hosted at https://sai.aiml.team, and the full local application is publicly available for free on GitHub through https://github.com/xdynames/sai‐app.</description><subject>applied deep learning</subject><subject>Arabidopsis</subject><subject>Cereal crops</subject><subject>Computer vision</subject><subject>Computers</subject><subject>convolutional neural network</subject><subject>Density</subject><subject>Humans</subject><subject>Inspection</subject><subject>Measurement</subject><subject>Microscopy</subject><subject>Phenotype</subject><subject>phenotyping</subject><subject>Plant physiology</subject><subject>Plant Stomata - physiology</subject><subject>Stomata</subject><issn>0028-646X</issn><issn>1469-8137</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp1kc9q3DAQh0VISbabHPICRZBLenAiWbIs9baEtLsQ2kASyM0o8qjVYluuZCf41kfoM_ZJqv3THgrVZRjNp49BP4TOKLmk6Vx1_bdLKktRHKAZ5UJlkrLyEM0IyWUmuHg6Rm9jXBNCVCHyI3TMhJCM83yGXu8H3-rF6gPWHQZrnXHQDamp8Rgh_Prx04Z0UzcTHrxvsPUBt6DjGKDdgN7iuDEMusG9DxC3T2voohumpHDd19RBj41v-3GAgF9cdL47QW-sbiKc7uscPX68ebheZrdfPq2uF7eZYVIWmSzA1JI_A-MFEK5AKmMEKFDMKipqTjnJBdXS1kpAyXMFacCMKajQnFM2Rxc7bx_89xHiULUuGmga3YEfY5WXQkpalJwl9PwfdO3H0KXtEiVLuqFkot7vKBN8jAFs1QfX6jBVlFSbNKqURrVNI7Hv9sbxuYX6L_nn-xNwtQNeXQPT_03V57vlTvkb54KVmA</recordid><startdate>202304</startdate><enddate>202304</enddate><creator>Sai, Na</creator><creator>Bockman, James Paul</creator><creator>Chen, Hao</creator><creator>Watson‐Haigh, Nathan</creator><creator>Xu, Bo</creator><creator>Feng, Xueying</creator><creator>Piechatzek, Adriane</creator><creator>Shen, Chunhua</creator><creator>Gilliham, Matthew</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H95</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2840-2533</orcidid><orcidid>https://orcid.org/0000-0003-0666-3078</orcidid><orcidid>https://orcid.org/0000-0002-9573-6082</orcidid><orcidid>https://orcid.org/0000-0002-7935-6151</orcidid><orcidid>https://orcid.org/0000-0002-7958-5771</orcidid><orcidid>https://orcid.org/0000-0002-9482-6115</orcidid><orcidid>https://orcid.org/0000-0003-4417-614X</orcidid><orcidid>https://orcid.org/0000-0002-8648-8718</orcidid><orcidid>https://orcid.org/0000-0002-7583-2384</orcidid></search><sort><creationdate>202304</creationdate><title>StomaAI: an efficient and user‐friendly tool for measurement of stomatal pores and density using deep computer vision</title><author>Sai, Na ; Bockman, James Paul ; Chen, Hao ; Watson‐Haigh, Nathan ; Xu, Bo ; Feng, Xueying ; Piechatzek, Adriane ; Shen, Chunhua ; Gilliham, Matthew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3885-85ecd84be345e049e89cc6e9e93f916d4140261a8fd96e7429e3f93cc516a4413</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>applied deep learning</topic><topic>Arabidopsis</topic><topic>Cereal crops</topic><topic>Computer vision</topic><topic>Computers</topic><topic>convolutional neural network</topic><topic>Density</topic><topic>Humans</topic><topic>Inspection</topic><topic>Measurement</topic><topic>Microscopy</topic><topic>Phenotype</topic><topic>phenotyping</topic><topic>Plant physiology</topic><topic>Plant Stomata - physiology</topic><topic>Stomata</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sai, Na</creatorcontrib><creatorcontrib>Bockman, James Paul</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><creatorcontrib>Watson‐Haigh, Nathan</creatorcontrib><creatorcontrib>Xu, Bo</creatorcontrib><creatorcontrib>Feng, Xueying</creatorcontrib><creatorcontrib>Piechatzek, Adriane</creatorcontrib><creatorcontrib>Shen, Chunhua</creatorcontrib><creatorcontrib>Gilliham, Matthew</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Ecology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>The New phytologist</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sai, Na</au><au>Bockman, James Paul</au><au>Chen, Hao</au><au>Watson‐Haigh, Nathan</au><au>Xu, Bo</au><au>Feng, Xueying</au><au>Piechatzek, Adriane</au><au>Shen, Chunhua</au><au>Gilliham, Matthew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>StomaAI: an efficient and user‐friendly tool for measurement of stomatal pores and density using deep computer vision</atitle><jtitle>The New phytologist</jtitle><addtitle>New Phytol</addtitle><date>2023-04</date><risdate>2023</risdate><volume>238</volume><issue>2</issue><spage>904</spage><epage>915</epage><pages>904-915</pages><issn>0028-646X</issn><eissn>1469-8137</eissn><abstract>Summary
Using microscopy to investigate stomatal behaviour is common in plant physiology research. Manual inspection and measurement of stomatal pore features is low throughput, relies upon expert knowledge to record stomatal features accurately, requires significant researcher time and investment, and can represent a significant bottleneck to research pipelines.
To alleviate this, we introduce StomaAI (SAI): a reliable, user‐friendly and adaptable tool for stomatal pore and density measurements via the application of deep computer vision, which has been initially calibrated and deployed for the model plant Arabidopsis (dicot) and the crop plant barley (monocot grass).
SAI is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets.
SAI boosts the number of images that can be evaluated in a fraction of the time, so can obtain a more accurate representation of stomatal traits than is routine through manual measurement. An online demonstration of SAI is hosted at https://sai.aiml.team, and the full local application is publicly available for free on GitHub through https://github.com/xdynames/sai‐app.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>36683442</pmid><doi>10.1111/nph.18765</doi><tpages>915</tpages><orcidid>https://orcid.org/0000-0002-2840-2533</orcidid><orcidid>https://orcid.org/0000-0003-0666-3078</orcidid><orcidid>https://orcid.org/0000-0002-9573-6082</orcidid><orcidid>https://orcid.org/0000-0002-7935-6151</orcidid><orcidid>https://orcid.org/0000-0002-7958-5771</orcidid><orcidid>https://orcid.org/0000-0002-9482-6115</orcidid><orcidid>https://orcid.org/0000-0003-4417-614X</orcidid><orcidid>https://orcid.org/0000-0002-8648-8718</orcidid><orcidid>https://orcid.org/0000-0002-7583-2384</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | The New phytologist, 2023-04, Vol.238 (2), p.904-915 |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Wiley Online Library Free Content; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | applied deep learning Arabidopsis Cereal crops Computer vision Computers convolutional neural network Density Humans Inspection Measurement Microscopy Phenotype phenotyping Plant physiology Plant Stomata - physiology Stomata |
| title | StomaAI: an efficient and user‐friendly tool for measurement of stomatal pores and density using deep computer vision |
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