<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>8692</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>SPIE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine-learning-based detection and severity estimation of drought stress in plants using hyperspectral imaging data</title>
    <abstract language="eng">Growing food demand due to population growth, coupled with increasingly frequent and severe droughts caused by climate change make water increasingly scarce. To address this, accurate assessment of plant water demand is essential for precise drought treatment and water conservation. Hyperspectral imaging (HSI) captures hypercubes, a combination of spectral and spatial data and offers promising capabilities for detection of plant stresses. However, most reported approaches only use selected spectral bands or indices, neglecting the full hypercube information. This is assumed to limit the detection accuracy. To overcome these limitations, we aim to develop a measurement pipeline to generate a comprehensive dataset comprising hypercubes of plants under varying drought stress levels along with selected physiological, environmental, and illumination data. This dataset will be used to train suitable data-driven models that enable improved drought stress detection as well as the non-invasive determination of physiological parameters based on HSI data.</abstract>
    <parentTitle language="eng">Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII</parentTitle>
    <identifier type="doi">10.1117/12.3072011</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T21:28:11Z","timestamp":1761859691862,"version":"build-2065373602"},"reference-count":0,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,30]]},"DOI":"10.1117\/12.3072011","type":"proceedings-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T21:21:40Z","timestamp":1761859300000},"page":"20","source":"Crossref","is-referenced-by-count":0,"title":["Machine-learning-based detection and severity estimation of drought stress in plants using hyperspectral imaging data"],"prefix":"10.1117","author":[{"given":"Lukas","family":"Bauer","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Vitzthumecker","sequence":"additional","affiliation":[]},{"given":"Rudolf","family":"Bierl","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"Ehrnsperger","sequence":"additional","affiliation":[]}],"member":"189","event":{"name":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII","start":{"date-parts":[[2025,9,15]]},"location":"Madrid, Spain","end":{"date-parts":[[2025,9,19]]}},"container-title":["Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII"],"original-title":[],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T21:22:59Z","timestamp":1761859379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/13666\/3072011\/Machine-learning-based-detection-and-severity-estimation-of-drought-stress\/10.1117\/12.3072011.full"}},"subtitle":[],"editor":[{"given":"Christopher M.","family":"Neale","sequence":"additional","affiliation":[]},{"given":"Antonino","family":"Maltese","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Castagna","sequence":"additional","affiliation":[]},{"given":"Charles R.","family":"Bostater","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,10,30]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1117\/12.3072011","relation":{},"subject":[],"published":{"date-parts":[[2025,10,30]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonEditorFirstName_1,PersonEditorLastName_1,PersonEditorFirstName_2,PersonEditorLastName_2,PersonEditorFirstName_3,PersonEditorLastName_3,PersonEditorFirstName_4,PersonEditorLastName_4,PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,ArticleNumber,CompletedYear</enrichment>
    <enrichment key="ConferenceStatement">SPIE Environmental Remote Sensing, 15.-18. September 2025, Madrid</enrichment>
    <enrichment key="OtherSeries">Proceedings of SPIE Volume 13666</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Lukas Bauer</author>
    <author>Thomas Vitzthumecker</author>
    <author>Rudolf Bierl</author>
    <author>Matthias Ehrnsperger</author>
    <collection role="institutes" number="FakANK">Fakultät Angewandte Natur- und Kulturwissenschaften</collection>
    <collection role="institutes" number="">Sensorik-Applikationszentrum (SappZ)</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="">Materialien und Produktion</collection>
  </doc>
</export-example>
