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

Sensory methods applied to ornamental plants enable studying more objectively plant visual quality key 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.