Analyzing the Fine Tuning’s impact in Grapevine Classification

Abstract

Wine is one the most important products from Portugal, being the grapevine variety very important to ensure uniqueness, authenticity and classification. In the Douro Demarcated Region, only certain grapevine varieties are allowed, implying the need for an identification mechanism. The ampelographs, professionals that use visual analysis to classify grapevines, are disappearing. In this situation, one possible replacement for ampelographs can be deep learning models. In previous experiments, we successfully classified 12 grapevines varieties, fine-tuning the Xception model, achieving ~0.9 in F1 score, raising the question, “what is the impact of the fine-tuning layers’ configuration in our results?”.This paper presents an analysis of the impact of different layers’ configuration in fine-tuning Xception model to classify 12 grapevine varieties with images acquired in a natural environment. Despite the model achieved F1-score of 0.92 in all configurations, using the Grad-CAM approach, we show that layers’ configuration in fine-tuning implies the quality of the models’ prediction. As analysis’ result, we can see that the model acting as feature extractor and fully fine-tuned obtains similar results in terms of metrics and pixel contribution, and fine-tuning only the last two blocks lead the model to look at more features in the image.