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

Journal article


Duman, S., Elewi, A., Hajhamed, A., Khankan, R., Souag, A. and Ahmed, A. 2024. 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.2024.111074
AuthorsDuman, S., Elewi, A., Hajhamed, A., Khankan, R., Souag, A. and Ahmed, A.
Abstract

State-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.

KeywordsOyster mushroom; Mushroom maturity; Smart farming; Precision agriculture; Image classification; Feature extraction; YOLO; PASCAL; VOC
Year2024
JournalData in Brief
PublisherElsevier
ISSN2352-3409
Digital Object Identifier (DOI)https://doi.org/10.1016/j.dib.2024.111074
Official URLhttps://doi.org/10.1016/j.dib.2024.111074
FunderCouncil for At-Risk Academics (Cara)
Publication dates
Online28 Oct 2024
Publication process dates
Accepted21 Oct 2024
Deposited07 Nov 2024
Publisher's version
License
File Access Level
Open
Output statusIn press
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