The selection of native greening plants to improve rural greening technology is crucial for enriching methods of building rural plant landscapes. However, there are few studies from the perspective of visual preference using quantitative methods. By using eye-tracking technology, this study studies students in the Central South University of Forestry and Technology and villagers in Changkou Village, Fujian Province, employing pictures of plant organs—leaves, flowers, and fruits—as stimulating materials to analyze five indicators: the total duration of fixations, the number of fixations, average duration of fixations, average pupil size and average amplitude of saccades.
A number of findings came from this research First, people visually prefer leaves, followed by flowers and fruits. In terms of species, Photinia × fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Cunninghamia lanceolata and Koelreuteria bipinnata have higher overall preference. Families such as Malvaceae, Fabaceae, Araliaceae, Myricaceae and Cupressaceae have stronger visual attraction than others.
Second, there are distinct differences in the preference of shapes and textures of leaves: aciculiform, strip, cordiform, sector and jacket-shape are more attractive; leather-like leaves have a higher visual preference than paper-like leaves; different colors and whether leaves are cracked or not have little effect on leaf observation.
Third, the preference for flowers with different inflorescence and colors is significant. Capitulum, cymes and panicles are more attractive; purple is the most preferred color, followed by white, yellow and red. Finally, there are significant differences in preferences for fruit characteristics, with medium-sized fruits and black fruits preferred, while kidney-shaped and spoon-shaped fruits are considered more attractive. Pomes, pods, samaras, and berries have received relatively more attention.
Read the complete research at www.nature.com.
Ding, N., Zhong, Y., Li, J. et al. Study on selection of native greening plants based on eye-tracking technology. Sci Rep 12, 1092 (2022). https://doi.org/10.1038/s41598-022-05114-0