Neural networks allow the automatic verification of the type of flour, analysing the starch granule morphology, to ensure the protected geographical indication ‘Galician Bread’
Xosé R. Fdez-Vidal, Nerea Fernández‐Canto, Ma Ángeles Romero‐Rodríguez, Ana María Ramos‐Cabrer, Santiago Pereira‐Lorenzo, Matilde Lombardero
Abstract
Quality control of flour is essential to control the quality of bread produced from it. We propose a control method based on the morphological characteristics of the granules of starch. The automation of the identification, segmentation and determination of the average size of the granules of starch of each of the cereals that make up a flour, from microscopy images, is an essential procedure for producers who want to produce bread under the protected geographical indication (PGI) ‘Galician Bread’. This identification and counting procedure, if performed manually, is a tedious activity for a trained expert, and is very time consuming. Thus, automating this task would streamline the process, in addition to saving a great deal of time. This paper addresses this problem by using deep learning approaches (Mask R–CNN) to predict the type of the granule of starch and its size for the first time. The trained models are then evaluated with the same raw microscopy images of these granules observed under polarized light, as has been previouly used for manual identification and counting. A dataset comprising 1308 2564 × 1924-pixel images is analysed. The images contain 17000 labelled granules of starch for two types of wheat: commercial wheat flour from ‘Castilla’ (type 0) and the Galician autochthonous flour ‘Caaveiro’ (type 1). The number of samples is approximately the same for each class. Instance segmentation with Mask R–CNN (Model II) achieved valid results for unseen images, with a categorical global accuracy of about 88.6% and with a discrepancy with respect to the areas of the granules as estimated by a human expert of less than 4%. The performance achieved by Mask R–CNN produces a strong correlation between the results of an expert and the results of the network, confirming the practical validity of our proposal.