Maturity Classification of Blueberry Fruit Using YOLO and Vision Transformer for Agricultural Assistance

This paper proposes a method for classifying the maturity levels of blueberry fruit from camera images as part of a cultivation support system. Following the five-stage maturity classification, the proposed approach first detects individual blueberry regions in an image and subsequently classifies each region into one of the five levels. The method leverages a Transformer-based model to extract features from local fruit regions that include contextual background, enabling the learning of spatial relationships both within and beyond the fruit boundaries. A dedicated dataset was constructed by capturing images of blueberry fruits alongside a color chart representing maturity levels. Experimental evaluations involving multiple deep learning models under three training–testing configurations demonstrate the effectiveness of the proposed method, achieving an average classification accuracy of 93.7%.