Estimation of Non-Invasive Grape Ripeness and Sweetness from Images Captured by a General-Purpose Camera

This paper examines the potential of deep learning methods for grape quality evaluation, particularly determining ripeness suitable for harvest and estimating grape “sweetness,” using imagery. The uniqueness of this study lies in its exclusive use of images captured with standard cameras to predict the quality of an edible green grape variety known as “Shine Muscat.” This approach anticipates future scenarios where farmers can easily capture images using smart glasses or smartphones. The method we developed leverages three channels from the RGB color space and CIELAB color space of the grape image, utilizes a feature extractor employing a vision transformer-based model, and introduces a deep learning model training method using a reference color. These innovations have successfully elevated the accuracy of the harvest determination task to 87.7%. Additionally, though the accuracy is not yet high, the paper showcases the potential of estimating grape “sweetness” directly from images by using the color estimation model as a pre-training model.