Exploring the relationship between landscape features and brain activation using vision AI

Y.H. Tung, C.Y. Chang
This research aims to investigate landscape features depicted by machine learning correlated with brain activation on the emotional response. Functional magnetic resonance imaging (fMRI) was used to scan participants' brains while viewing various types of environmental images. The analysis focuses on emotion-related brain activation which is related to emotion and mental health. By using the Google Vision AI, this study tried to identify labeled visual elements of the images by AI feature detection (REST and RPC API) to understand what environmental features potentially predict emotion response based on the neuroimaging data. The study explored the use of feature detection and fMRI data to construct research on the visual landscape assessment field. This method is expected to be more accurate and objectively reveal the relationship between human cognitive health and environmental factors. While the COVID-19 pandemic has accelerated human demands for new technology, it has also given rise to new possible applications of artificial intelligence, this research corresponds to the use of AI and the neuro-activation that could become a new resource of many decision-making grounds.
Tung, Y.H. and Chang, C.Y. (2021). Exploring the relationship between landscape features and brain activation using vision AI. Acta Hortic. 1330, 153-160
DOI: 10.17660/ActaHortic.2021.1330.17
https://doi.org/10.17660/ActaHortic.2021.1330.17
landscape feature detection, amygdala, mental health
English

Acta Horticulturae