Grape maturity estimation based on seed images and neural networks

• A method for estimating grape maturity is presented.
• The method is based on neural networks and seed images.
• The images are obtained through an optical scanner.
• A robust to shadows and highlights segmentation method is developed.
• The proposed method has a high hit rate.

The grape phenolic maturity is one of the most important parameters to determine the optimal time for harvest. In this paper we propose an innovative methodology for the problem of how this task is performed today. In particular, the method consists in analyzing seed images using pattern recognition methodology, and classifying them in immature, mature and over mature states through a supervised learning neural network. The methodology presented gives objective information about maturity, which is useful for deciding the moment when the harvest should be performed.