Assessing the visual aspect of rotating virtual rose bushes by a labeled sorting task

Aesthetics is one of the major parameters for consumers when buying a rose bush. Therefore, managing this quality is important for agronomists. Tools are needed to assess visual characteristics and to find links with architectural plant parameters. Sensory analyses were developed using real plants and photographs as stimuli. With technology and modeling improvements, using virtual plants could presents numerous advantages. This study demonstrated the feasibility of using rotating virtual rose bush videos as stimuli for a labeled sorting task. The virtual rose bush reflected a natural within-crop variability of one cultivar based on bud breaks location and axes length. Two panels of subjects closely linked to the horticulture sector sorted and described 40 rotating virtual rose bush videos. Non-metric Multidimensional Scaling (MDS) results for both panels were similar and allowed us to highlight five groups of virtual rose bushes with their specific sensory characteristics and their own most representative products using a combination of the paragons and the most typical products. This approach revealed that subjects detected high visual differences between products, and that by using rotation, they were able to integrate 3D properties about variations around plant facets. Finally, a labeled sorting task is a powerful method for preliminary exploration of the visual aspect of virtual plants

A new approach to predict the visual appearance of rose bush from image analysis of 3D videos

International audienceSensory methods applied to ornamental plants enable studying more objectively plant visual qualitykey drivers of consumer preferences. However, management upkeep of a trained panel for sensory profile is time-consuming, not flexible and represents non-negligible costs. The present paper achieves the proof of the concept about using morphometrical descriptors upkeep of a trained panel for sensory profile is time-consuming, not flexible and represents non-negligible costs. The present paper achieves the proof of the concept about using morphometrical descriptors integrating 2D image features from rotating virtual rose bush videos to predict their visual appearance according to different sensory attributes. Using real plants cultivated under a shading gradient and imaged in rotation during three development stages, acceptable prediction error of the sensory attributes ranging from 6.2 to 19.8% (normalized RMSEP) were obtained with simple ordinary least squares OLS) regression models and linearization. The most accurate model obtained was for the flower quantity perception.Finally, a secondary analysis highlighted in most of the studied traits a significant influence of defoliation, stressing herefore the impact of the leaves on plant architecture, and thus on the visual appearance