Classification of grapevine cultivars using Kirlian camera and machine learning

Abstract

The aim of the study was to verify whether Kirlian camera could be used to describe grapevines and if the berry bioelectric field is influenced by disease. With Kirlian camera we measured bioelectric fields of grape berries. To complete the measurements we described acquired coronas of the berries with numerical parameters and used machine learning algorithms to classify grapevine cultivars. We tested this method on eight grapevine cultivars, performing different tests. The results show that coronas of grapevine berries contain useful information about cultivar and their sanitary status.