Artificial vision system for the identification of ripeness of pasion fruit (granadilla)

Abstract

The proper handling of fruits has become one of the most important economic activities in the Colombian agriculture [1]. Actually, the identification of the ripeness of fruit is made manually [2], which induces variability due to subjectivity by expert eye strain. The purpose of this research was to develop a computational tool for identifying the state of ripeness of passion fruit (granadilla) through images recognition. The area in pixels of the fruit images was extracted by a technique called Otsu, using OpenCv libraries in Python. Finally, the task of classification was conducted through cluster analysis, here were assigned 110 points RGB belonging to each state of maturity of passion fruit. The results showed 92, 6% of accuracy for identifying the state of ripeness, from a set of 90 images obtained from 90 fruits in different stages of maturity, which was compared with traditional analysis (conducted by experts) according to the provisions of the Colombian Technical Standard NTC 4101.

Keywords

• agricultura colombiana
• aplicación de visión artificial
• frutas
• procesamiento de imágenes
• Python