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A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications

dc.contributor.authorDuman, Sonay
dc.contributor.authorElewi, Abdullah
dc.contributor.authorHajhamed, Abdulsalam
dc.contributor.authorKhankan, Rasheed
dc.contributor.authorSouag, Amina
dc.contributor.authorAhmed, Asma
dc.date.accessioned2024-11-29T08:21:18Z
dc.date.available2024-11-29T08:21:18Z
dc.date.issued2024
dc.description.abstractState-of-the-art technologies such as computer vision and machine learning, are revolutionizing the smart mushroom industry by addressing diverse challenges in yield prediction, growth analysis, mushroom classification, disease and deformation detection, and digital twinning. However, mushrooms have long presented a challenge to automated systems due to their varied sizes, shapes, and surface characteristics, limiting the effectiveness of technologies aimed at mushroom classification and growth analysis. Clean and well-labelled datasets are therefore a cornerstone for developing efficient machine-learning models. Bridging this gap in oyster mushroom cultivation, we present a novel dataset comprising 555 high-quality camera raw images, from which approximately 16.000 manually annotated images were extracted. These images capture mushrooms in various shapes, maturity stages, and conditions, photographed in a greenhouse using two cameras for comprehensive coverage. Alongside the images, we recorded key environmental parameters within the mushroom greenhouse, such as temperature, relative humidity, moisture, and air quality, for a holistic analysis. This dataset is unique in providing both visual and environmental time-point data, organized into four storage folders: “Raw Images”; “Mushroom Labelled Images and Annotation Files”; “Maturity Labelled Images and Annotation Files”; and “Sensor Data”, which includes time-stamped sensor readings in Excel files. This dataset can enable researchers to develop high-quality prediction and classification machine learning models for the intelligent cultivation of oyster mushrooms. Beyond mushroom cultivation, this dataset also has the potential to be utilized in the fields of computer vision, artificial intelligence, robotics, precision agriculture, and fungal studies in general.en
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16939
dc.identifier.urihttps://doi.org/10.1016/j.dib.2024.111074
dc.language.isoeng
dc.rights.licensecc_by
dc.source2352-3409
dc.sourceData in Brief, 57 (2024), 111074
dc.subjectOyster mushroom
dc.subjectMushroom maturity
dc.subjectSmart farming
dc.subjectPrecision agriculture
dc.subjectImage classification
dc.subjectFeature extraction
dc.subjectYOLO
dc.subjectPASCAL VOC
dc.subject.ddc630
dc.titleA novel dataset of annotated oyster mushroom images with environmental context for machine learning applicationsen
dc.type.diniArticle
dcterms.bibliographicCitationData in Brief, 57 (2024), 111074. https://doi.org/10.1016/j.dib.2024.111074. ISSN: 2352-3409
dcterms.bibliographicCitation.articlenumber111074
dcterms.bibliographicCitation.issn2352-3409
dcterms.bibliographicCitation.journaltitleData in Brief
dcterms.bibliographicCitation.originalpublishernameElsevier
dcterms.bibliographicCitation.originalpublisherplaceAmsterdam
dcterms.bibliographicCitation.volume57
local.export.bibtex@article{Duman2024, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16939}, doi = {10.1016/j.dib.2024.111074}, author = {Duman, Sonay and Elewi, Abdullah and Hajhamed, Abdulsalam et al.}, title = {A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications}, journal = {Data in Brief}, year = {2024}, volume = {57}, }
local.export.bibtexAuthorDuman, Sonay and Elewi, Abdullah and Hajhamed, Abdulsalam et al.
local.export.bibtexKeyDuman2024
local.export.bibtexType@article

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