  In viticulture the epicuticular wax as the outer layer of the berry skin is
known as trait which is correlated to resilience towards Botrytis bunch rot.
Traditionally this trait is classified using the OIV descriptor 227 (berry
bloom) in a time consuming way resulting in subjective and error-prone
phenotypic data. In the present study an objective, fast and sensor-based
approach was developed to monitor berry bloom. From the technical
point-of-view, it is known that the measurement of different illumination
components conveys important information about observed object surfaces. A
Mobile Light-Separation-Lab is proposed in order to capture
illumination-separated images of grapevine berries for phenotyping the
distribution of epicuticular waxes (berry bloom). For image analysis, an
efficient convolutional neural network approach is used to derive the
uniformity and intactness of waxes on berries. Method validation over six
grapevine cultivars shows accuracies up to $97.3$%. In addition, electrical
impedance of the cuticle and its epicuticular waxes (described as an indicator
for the thickness of berry skin and its permeability) was correlated to the
detected proportion of waxes with $r=0.76$. This novel, fast and non-invasive
phenotyping approach facilitates enlarged screenings within grapevine breeding
material and genetic repositories regarding berry bloom characteristics and its
impact on resilience towards Botrytis bunch rot.
