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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.

Mushrooms are a nutritious food source, which can play a crucial role in providing affordable sources of proteins, vitamins and minerals for people worldwide, but their cultivation requires extensive training and considerable relevant expertise in order to fine-tune multiple environmental parameters.Internally displaced people in the Northern regions of Syria rely on very small-scale traditional oyster mushroom production, which cannot meet their local demand. Many international and local non-governmental organizations (NGOs) working for Syrian refugees, work on mushroom cultivation projects. They have reported significant difficulties and challenges in mushroom cultivation amongst the targeted beneficiaries. Therefore, the two main questions driving this research are: (1) How can organic mushroom cultivation be promoted using a robust and affordable intelligent mushroom farming system? (2) How can organic mushroom farming practices be simplified to support internally displaced and refugee Syrians?This research evaluates the process of automating mushroom cultivation by designing and implementing a smart oyster (Pleurotus ostreatus) mushroom farming system to remotely monitor and manage environmental parameters, such as temperature, humidity, air quality and illumination, inside the farm. Furthermore, ready and dedicated user-friendly web interfaces were also implemented to enable farmers to remotely monitor and manage their farms through the Internet. As a result, a dependable and cost-effective intelligent oyster mushroom cultivation system was designed and implemented in this work. The system includes remote monitoring and management via user-friendly interfaces. This simplifies mushroom cultivation for not only refugees and displaced communities, but also for mushroom farmers in low-income countries. This work can contribute to the eradication of poverty and hunger, in line with the United Nations Sustainable Development Goals one and two.