@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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<https://fis.th-koeln.de/vivo/individual/10000146>
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        rdfs:label  "Institut für Produktentwicklung und Konstruktionstechnik" ;
        <http://cris.nrw/hisinone/istOrganisationseinheitVon>
                <https://fis.th-koeln.de/vivo/individual/publ_12439> .

<https://fis.th-koeln.de/vivo/individual/publ_12439>
        a                         obo:IAO_0000030 , thkoeln:Publikation , obo:BFO_0000001 , owl:Thing , foaf:Document , thkoeln:Artikel , bibo:Document , thkoeln:Journalartikel , obo:BFO_0000031 , obo:BFO_0000002 ;
        rdfs:label                "Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms" , "RDF description of Detection of Growth Stages of Chilli Plants in a Hydroponic Grower Using Machine Vision and YOLOv8 Deep Learning Algorithms - https://fis.th-koeln.de/vivo/individual/publ_12439" ;
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        thkoeln:sprache           "englisch" ;
        thkoeln:status            "validiert" ;
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                <https://fis.th-koeln.de/vivo/individual/cite_12439-2> , <https://fis.th-koeln.de/vivo/individual/cite_12439-0> , <https://fis.th-koeln.de/vivo/individual/cite_12439-1> ;
        <http://cris.nrw/hisinone/hatZugangsrecht>
                <http://cris.nrw/hisinone/OpenAccess> ;
        <http://purl.org/dc/elements/1.1/date>
                "2026-04-04T01:11:03"^^xsd:dateTime ;
        <http://purl.org/dc/elements/1.1/publisher>
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        <http://purl.org/dc/elements/1.1/rights>
                <https://fis.th-koeln.de/vivo/termsOfUse> ;
        bibo:abstract             "Vertical indoor farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use deep learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s-eye view and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering, and fruiting classes. All the trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance." ;
        bibo:doi                  "10.3390/su16156420" ;
        bibo:issue                "15" ;
        bibo:volume               "16" ;
        vitro:mostSpecificType    thkoeln:Journalartikel ;
        vivo:dateTimeValue        <http://cris.nrw/date2024> ;
        vivo:freetextKeyword      "machine vision" , "chilli plants" , "hydroponics" , "image processing" , "Capsicum annuum" , "artificial intelligence" , "indoor farming" , "deep learning" , "YOLOv8" ;
        vivo:hasPublicationVenue  <https://fis.th-koeln.de/vivo/individual/journal_2140> ;
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        a       thkoeln:Zitat , owl:Thing .

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        rdfs:label  "Publikationen"@de-DE , "Publications"@en-US .

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