[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117214-en":3,"doc-seo-117214-105":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},117214,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications","A novel dataset is presented to support machine learning for intelligent oyster mushroom cultivation, combining visual data with greenhouse environmental context. It includes 555 high-quality camera raw images from which approximately 16,000 manually annotated images were extracted, covering multiple mushroom shapes, maturity stages, and growing conditions. Images were captured in a greenhouse with two cameras and paired with time-stamped sensor measurements (temperature, relative humidity, and air quality, plus temperature/moisture inside composite bags) organized for accessible storage and model development.","Q1  \nQ2  \n ARTICLE IN PRESS   \nJID: DIB [mUS1Ga;November 1, 2024;13:13]  \nData in Brief xxx (xxxx) xxx  \nContents lists available at ScienceDirect  \nData in Brief  \njournal [homepage: www.elsevier.com/locate/dib](homepage: www.elsevier.com/locate/dib)  \nData Article  \nA novel dataset of annotated oyster mushroom images with environmental context for machine learning applications  \nSonay Dumana,c, Abdullah Elewia, Abdulsalam Hajhamed b,∗, Rasheed Khankand, Amina Souage, Asma Ahmed f  \na Computer Engineering Department, Mersin University, 33343 Mersin, Turkey b Faculty of Agricultural Sciences, University of Hohenheim, 70599 Stuttgart, Germany c Software Engineering Department, Toros University, 33140 Mersin, Turkey  \nd Electrical and Electronic Engineering Department, Mersin University, 33343 Mersin, Turkey  \ne School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury, United Kingdom f Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom  \na r t i c l e i n f o a b s t r a c t  \nArticle history:  \nReceived 15 July 2024  \nRevised 3 October 2024  \nAccepted 21 October 2024  \nAvailable online xxx  \nDataset link: Annotated oyster mushroom images (Original data)  \nKeywords:  \nOyster mushroom Mushroom maturity Smart farming Precision agriculture Image classification Feature extraction YOLO  \nPASCAL VOC  \n∗ Corresponding author.  \nState-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 welllabelled 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 hu-  \nE-mail address: [abdulsalam.hajhamed@uni-hohenheim.de](abdulsalam.hajhamed@uni-hohenheim.de) (A. Hajhamed).  \n[https://doi.org/10.1016/j.dib.2024.111074](https://doi.org/10.1016/j.dib.2024.111074)  \n2352-3409/© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \nPlease cite this article as: S. Duman, A. Elewi and A. Hajhamed et al., A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications, Data in Brief, [https://doi.org/10.1016/j.dib.2024](https://doi.org/10.1016/j.dib.2024). 111074  \n ARTICLE IN PRESS   \nJID: DIB [mUS1Ga;November 1, 2024;13:13]  \n2 S. Duman, A. Elewi and A. Hajhamed et al. /Data in Brief xxx (xxxx) xxx  \nmidity, 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 highquality prediction and classification machine learning models for the intelligent cultivation of oyster mushrooms. Beyond mushroom cultivation, this dataset also has the potential tobe utilized in the fields ","cbCaiiXSivKw8sZZ","https://ap.wps.com/l/cbCaiiXSivKw8sZZ","pdf",2310639,1,10,"English","en",105,"# Specifications Table\n## Data collection and annotation process\n## Data source location and data accessibility\n# Value of the Data\n## Importance of diverse mushroom features","[{\"question\":\"What does the dataset contain?\",\"answer\":\"The dataset includes annotated oyster mushroom images plus time-stamped environmental sensor data captured in a greenhouse. It also provides organized storage folders such as raw images, labeled images/annotations for mushrooms and maturity, and sensor data in Excel files.\"},{\"question\":\"How were the images captured and annotated?\",\"answer\":\"Images were captured using two TP-Link Tapo C310 IP cameras during cultivation cycles, then manually extracted from raw images and annotated. The dataset covers different mushroom shapes and maturity stages using multiple annotation formats.\"},{\"question\":\"Which environmental parameters are recorded alongside the images?\",\"answer\":\"Environmental parameters include greenhouse temperature, relative humidity, and air quality, as well as temperature and moisture inside mushroom composite bags. Sensor readings are time-stamped to link with the corresponding images.\"}]","A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications | PDF",1785674438,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-novel-dataset-of-annotated-oyster-mushroom-images-with-environmental-context-for-machine-learning-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-novel-dataset-of-annotated-oyster-mushroom-images-with-environmental-context-for-machine-learning-applications/117214/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the dataset contain?","Question",{"text":75,"@type":76},"The dataset includes annotated oyster mushroom images plus time-stamped environmental sensor data captured in a greenhouse. It also provides organized storage folders such as raw images, labeled images/annotations for mushrooms and maturity, and sensor data in Excel files.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the images captured and annotated?",{"text":80,"@type":76},"Images were captured using two TP-Link Tapo C310 IP cameras during cultivation cycles, then manually extracted from raw images and annotated. The dataset covers different mushroom shapes and maturity stages using multiple annotation formats.",{"name":82,"@type":73,"acceptedAnswer":83},"Which environmental parameters are recorded alongside the images?",{"text":84,"@type":76},"Environmental parameters include greenhouse temperature, relative humidity, and air quality, as well as temperature and moisture inside mushroom composite bags. 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