Automated phenotyping of epicuticular waxes of grapevine berries using light separation and convolutional neural networks

•Convolution Neural Networks are used to phenotype epicuticular waxes.•Light separation methods are used which increase the accuracy of wax detection.•Monitoring of berry bloom with a fast, objective and sensor-based approach. The epicuticular wax represents the outer layer of the grape berry skin and is known as trait that is significantly 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 epicuticular waxes. From the technical point-of-view, it is known that the measurement of different illumination components conveys important information about observed object surfaces. A 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.