Artificial Vision Techniques for Strawberry's Industrial Classification

This research presents novel artificial vision techniques applied to the detection of features for strawberries used in the food industry. For this purpose, a computer vision system based in artificial neural networks is used, organized as a deep architecture and trained with noise compensated learning. This combination originates a strong network - object relations which makes possible the recognition of complex strawberry features under changing conditions of lightning, size and orientation. The programming uses OpenCV libraries and fruits databases captured with a webcam. The images used to train the Artificial Neural Network are defined with canny edge detection and a moving region of interest (ROI). After training, the network recognizes important features such as shape, color and anomalies. The system has been tested in real time with real images.