A method to estimate Grape Phenolic Maturity based on seed images

• Estimation of seed Phenolic Maturity based on seed images.
• Segmentation proposal is based on supervised and unsupervised learning.
• Segmentation method robust to highlights and cast shadows.
• Maturity seed classification uses a simple neural network.
• Results allow a real implementation.

The timing of the grape harvest has a strong impact on wine quality. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a method is presented to estimate Grape Phenolic Maturity based on seed images. The acquired images present problems such as shadows, highlights and low contrast. Two classes of seed are defined (mature and immature) by the expert (enologist) involved in the research. The method consists of three stages: segmentation, feature extraction and classification. Segmentation was performed by a hybrid method combining supervised and unsupervised learning, feature extraction by the Sequential Forward Selection algorithm, and classification by a Simple Perceptron. The results for each stage are presented. The method as a whole proved to be simple and effective in the classification of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions.