Toward Grapevine Digital Ampelometry Through Vision Deep Learning Models

Abstract

Several thousand grapevine varieties exist, with even more naming identifiers. Adequate specialised labour is not available for proper classification or identification of grapevines, making the value of commercial vines uncertain. Traditional methods such as genetic analysis or ampelometry are time-consuming, expensive and often require expert skills that are even rarer. New vision-based systems benefit from advanced and innovative technology and can be used by non-experts in ampelometry. To this end, Deep Learning (DL) and Machine Learning (ML) approaches have been successfully applied for classification purposes. The present work extends the state-of-the-art by applying digital ampelometry techniques to larger grapevine varieties. We benchmarked MobileNet v2, ResNet-34 and VGG-11-BN DL classifiers to assess their ability for digital ampelography. In our experiment, all the models could identify the vines’ varieties through the leaf with a weighted F1 score higher than 92 %.