{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:45:51Z","timestamp":1770036351401,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DGE-1735362"],"award-info":[{"award-number":["DGE-1735362"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Agriculture and Food Research Initiative","award":["2023-67021-38977"],"award-info":[{"award-number":["2023-67021-38977"]}]},{"DOI":"10.13039\/100000199","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["1029656"],"award-info":[{"award-number":["1029656"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["NEB-21-176"],"award-info":[{"award-number":["NEB-21-176"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"USDA National Institute of Food and Agriculture","doi-asserted-by":"crossref","award":["1029656"],"award-info":[{"award-number":["1029656"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000199","name":"Cyber-Physical Systems Plant Health and Production and Plant Products","doi-asserted-by":"publisher","award":["NEB-21-176"],"award-info":[{"award-number":["NEB-21-176"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Advancements in phenotyping technologies, including object imaging, high-throughput monitoring, and soft computing, are pivotal for understanding plant responses to environmental stresses. These technologies enable detailed analyses of morphological, physiological, and structural adaptations under abiotic and biotic stresses, such as drought. Current work using multimodal and multi-perspective image processing methods can capture the essential processes that enhance plant resilience and counteract stress by identifying morphological and biochemical indicators. However, the dynamic and complex nature of plant responses poses multiple challenges for generating precise analytics and descriptors of evolving phenotypes. This work introduces analytics for concurrent imaging, adopting the underlying principle of cosegmentation to create taxonomies for new phenotypes. Here, unidimensional refers to the concurrent analysis of multiple images within a single phenotyping dimension: temporal, modal, or perspective, rather than combining information across dimensions. The proposed unidimensional phenotypes integrate concurrent images within individual temporal, modal, or perspective dimensions to capture dynamic morphological and physiological responses that are not observable with conventional single-image or cumulative metrics. Within a high-throughput imagery production system, these phenotypes enable more nuanced quantification of phenotypic changes, leveraging the strengths of simultaneous image analysis to enhance insight into plant adaptations. This workflow aligns with the investigation of plants\u2019 adaptive strategies under abiotic stress and provides quantitative indicators of plant health under adverse environmental conditions.<\/jats:p>","DOI":"10.3390\/plants15030428","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T14:35:46Z","timestamp":1769783746000},"page":"428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Introducing Concurrent Imaging and Unidimensional Analytics for Plant Stress Responses"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5944-4528","authenticated-orcid":false,"given":"Rubi","family":"Qui\u00f1ones","sequence":"first","affiliation":[{"name":"Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA"},{"name":"School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6613-6766","authenticated-orcid":false,"given":"Francisco","family":"Mu\u00f1oz-Arriola","sequence":"additional","affiliation":[{"name":"School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"},{"name":"Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9639-5550","authenticated-orcid":false,"given":"Sruti","family":"Das Choudhury","sequence":"additional","affiliation":[{"name":"School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"},{"name":"School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4559-9454","authenticated-orcid":false,"given":"Ashok","family":"Samal","sequence":"additional","affiliation":[{"name":"School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"707","DOI":"10.2135\/cropsci2011.06.0299","article-title":"Genomic Prediction of Breeding Values when Modeling Genotype \u00d7 Environment Interaction using Pedigree and Dense Molecular Markers","volume":"52","author":"Weigel","year":"2012","journal-title":"Crop Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"iyac057","DOI":"10.1093\/genetics\/iyac057","article-title":"Genome-wide mediation analysis: An empirical study to connect phenotype with genotype via intermediate transcriptomic data in maize","volume":"221","author":"Yang","year":"2022","journal-title":"Genetics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5336","DOI":"10.1093\/jxb\/erac146","article-title":"Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability","volume":"73","author":"Sarzaeim","year":"2022","journal-title":"J. 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