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Biophilic design is a well-recognised discipline aimed at enhancing health and well-being, however, most buildings lack adequate representation of nature or nature-inspired art. Notable barriers exist such as wealth, education, and physical ability restricting people’s accessibility to nature and associated artworks. An AI-based Biophilic arts curation and personalised recommendation system were developed in this study to improve accessibility to biophilic arts. Existing Biophilic research mainly focuses on building design principles, limited research exists to examine biophilic arts and associated emotional responses. In this paper, an interdisciplinary study addresses this gap by developing metrics for Biophilic art attributes and potential emotional responses, drawing on existing Biophilic architecture attributes and PANAS items. A public survey of 200 participants was developed in this study. The survey collected art viewers’ ratings of Biophilic attributes and associated emotional responses to establish statistical correlations between Biophilic attributes and emotional responses. The statistical analysis established a positive correlation between Biophilic attributes and positive emotions. The public survey results show significant positive emotional impacts (p-value <0.05) after exposure to Biophilic images, supporting further research and development of the Biophilic art curation system. This digital curation system employs Computer Vision algorithms (ResNet50) to automate Biophilic art categorisation and generate personalised recommendations. This study emphasises the importance of integrating nature into built environments. It proposes that artificial intelligence could significantly enhance the categorisation and recommendation of Biophilic art, advocating for expanding Biophilic art databases for emotionally responsive art display systems, benefiting mental health, and making art more accessible.

Biophilia is an innate love for living things and nature itself that has been associated with a positive impact on mental health and well-being. This study explores the application of deep learning methods for the classification of Biophilic artwork, in order to learn and explain the different Biophilic characteristics present in a visual representation of a painting. Using the concept of Biophilia that postulates the deep connection of human beings with nature, we use an artificially intelligent algorithm to recognise the different patterns underlying the Biophilic features in an artwork. Our proposed method uses a lower-dimensional representation of an image and a decoder model to extract salient features of the image of each Biophilic trait, such as plants, water bodies, seasons, animals, etc., based on learnt factors such as shape, texture, and illumination. The proposed classification model is capable of extracting Biophilic artwork that not only helps artists, collectors, and researchers studying to interpret and exploit the effects of mental well-being on exposure to nature-inspired visual aesthetics but also enables a methodical exploration of the study of Biophilia and Biophilic artwork for aesthetic preferences. Using the proposed algorithms, we have also created a gallery of Biophilic collections comprising famous artworks from different European and American art galleries, which will soon be published on the Vieunite@ online community.