Comparison of deep learning methods for grapevine growth stage recognition

Abstract

Monitoring the phenological development stages of grapes represents a challenge in viticulture. It includes the phenological distinction of the growth stages of grapevines and the continuous technological developments, especially in computer vision, enabling a detailed classification of economically relevant development stages of grapes. In the present work, we show that based on a cascading computer vision approach, the development stages of grapes can be classified and distinguished at the micro level. In a comparative experiment (ResNet, DenseNet, InceptionV3), it could be shown that a ResNet architecture provides the best classification results with an average accuracy of 88.1%.

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