Fruit Identification and Quality Detection by Means of DAG-CNN

Abstract

The design of quality control systems in food has become essential in research to guarantee an adequate state for its consumption. It is necessary to develop automatic and efficient systems that can verify its state before its distribution. This paper presents an algorithm based on deep learning for the identification of fruits and the state they are in, oriented to changes in camera focus, capture angles, lighting variations, and change of backgrounds. In this case, 8 types of fruit are chosen to identify what kind of fruit is being observed and if it is in good condition or not, establishing a total of 16 categories that the network must classify. A convolutional neural network with a DAG structure is proposed for the learning of fruits and their state. A graphic user interface is designed to allow the acquisition of the image of the fruit and its subsequent classification in some of the categories. A 94.43% accuracy was obtained in the 1600 test images classification, with approximate processing times of 45-55 milliseconds. Therefore, it can be concluded that the proposed system based on Deep learning can adequately perform a process of detection of types of fruits and their state.