[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118182-en":3,"doc-seo-118182-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118182,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning","Autofluorescence-based imaging enables non-destructive characterization of plant biochemical and physiological properties driven by genotype. The study evaluates stress-tolerant versus stress-susceptible Brassica rapa genotypes under progressive drought using machine learning to classify stressed versus non-stressed tissue. Time-series autofluorescence spectral image sequences are used to train stress detection models and derive two novel stress-based image phenotypes—average percentage stress and moving average percentage stress—to capture temporal variation and support genotype discrimination.","School of Natural Resources: Faculty Publications  \nNatural Resources, School of  \n4-19-2024  \nStress phenotyping analysis leveraging autofluorescence image sequences with machine learning  \nSruti Das Choudhury Carmela Rosaria Guadagno Srinidhi Bashyam Anastasios Mazis  \nBrent E. Ewers  \nSee next page for additional authors  \nFollow this and additional works at: [https://digitalcommons.unl.edu/natrespapers](https://digitalcommons.unl.edu/natrespapers)  \n Part of the Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, and the Other Environmental Sciences Commons  \nThis Article is brought to you for free and open access by the Natural Resources, School of at DigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in School of Natural Resources: Faculty Publications by an authorized administrator of DigitalCommons@University of NebraskaLincoln.  \nAuthors  \nSruti Das Choudhury, Carmela Rosaria Guadagno, Srinidhi Bashyam, Anastasios Mazis, Brent E. Ewers, Ashok Samal, and Tala Awada  \nTYPE Methods  \nPUBLISHED 19 April 2024  \nDOI 10.3389/fpls.2024.1353110  \nOPEN ACCESS  \nEDITED BY  \nMansour Ghorbanpour, Arak University, Iran  \nREVIEWED BY  \nFatemeh Bovand,  \nIslamic Azad University of Arak, Iran Ghasem Eghlima  \nShahid Beheshti University, Iran  \n*CORRESPONDENCE Sruti Das Choudhury  \n [S. D.Choudhury@unl.edu](S. D.Choudhury@unl.edu)  \n†These authors have contributed equally to this work  \nRECEIVED 09 December 2023  \nACCEPTED 14 March 2024  \nPUBLISHED 19 April 2024  \nCITATION  \nDas Choudhury S, Guadagno CR, Bashyam S, Mazis A, Ewers BE, Samal A and Awada T (2024) Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.  \nFront. Plant Sci. 15:1353110 .  \ndoi: 10.3389/fpls.2024.1353110  \nCOPYRIGHT  \n© 2024 Das Choudhury, Guadagno, Bashyam, Mazis, Ewers, Samal and Awada. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nStress phenotyping analysis leveraging autofluorescence image sequences with machine learning  \nSruti Das Choudhury 1,2*†, Carmela Rosaria Guadagno 3†, Srinidhi Bashyam 2, Anastasios Mazis 1, Brent E. Ewers 3, Ashok Samal 2 and Tala Awada 1,4  \n1School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States, 2School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States, 3 Department of Botany, University of Wyoming, Laramie, WY, United States, 4Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States  \nBackground: Autofluorescence-based imaging has the potential to nondestructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research.  \nMethods: Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from ","cbCaiitq0AjPmtEh","https://ap.wps.com/l/cbCaiitq0AjPmtEh","pdf",5419109,1,14,"English","en",105,"# Abstract\n# Introduction\n## Background and rationale\n# Methods\n## Data and classification approach\n## Derived stress phenotypes\n# Results\n## Genotypic discrimination performance\n# Conclusion\n## Key imaging signal and implications","[{\"question\":\"What imaging approach is used to study plant stress in this work?\",\"answer\":\"The study uses autofluorescence-based imaging with spectral image sequences from multiple Brassica rapa genotypes to characterize stress-related tissue changes.\"},{\"question\":\"How is machine learning applied to detect stress?\",\"answer\":\"Machine learning models are trained on autofluorescence spectral images to build a stress detection classifier that separates stressed versus non-stressed tissue.\"},{\"question\":\"What novel stress-related phenotypes does the analysis produce?\",\"answer\":\"Two stress-based image phenotypes are computed: the average percentage stress and the moving average percentage stress, designed to quantify temporal variation under progressive drought.\"}]","Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning | 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