Automatic classification of citrus aurantifolia based on digital image processing and pattern recognition

Citrus Aurantifolia swingle is grown on the northern coast of Peru for domestic consumption and export. This is an indispensable ingredient due to its high level of acidity for the preparation of fish ceviche, the traditional dish of Peruvian gastronomy. Lemons are classified according to their color in yellow, green and pinton (green lemons already showing a hint of yellow), since the yellow ones are for national consumption, while the other two types are for export. This selection is done manually. This process is time consuming and additionally lemons are frequently misclassified due to lack of concentration, exhaustion and experience of the worker, affecting the quality of the product sold in domestic and foreign markets. Therefore, this paper introduces a new method for the automatic classification of Citrus Aurantifolia, which comprises three stages: acquisition, image processing, feature extraction, and classification. A mechanical prototype for image acquisition in a controlled environment and a software for the classification of lemons were developed. A new segmentation method was implemented, which makes use only of the information obtained from the blue channel. From the segmented images we obtained the color characteristics, selecting the best descriptors in the RGB and CIELAB spaces, finding that the red channel allows the best accuracy. Two classification models were used, SVM and KNN, obtaining an accuracy of 99.04% with the K-NN.