Detection of Chemically Matured Mango Fruits Using Laplacian Descriptors and Scale Determinants

Abstract

Nowadays machine vision systems are largely used for Fruit harvesting, fruit tracking, fruit quality assessment, and detection of artificial ripening of fruits. Existing research concentrates on shape-based fruit harvesting using robots, 3D reconstruction of images for in field fruit tracking systems, non – destructive methods for quality assessment of fruits, and detection of artificially ripened fruits. Besides shape, the color of the fruit is a unique and protruding feature. Though a lot of effort is focused on harvesting, tracking, and quality assessment of mango fruits using these features, fewer efforts are made in the path of the identification of artificially matured mango fruits. Simpler, robust, and easily accessible solutions are desired by exploiting both RGB descriptors and feature point locators. Here a new context for detection of artificially matured mango fruit based on SURF descriptors is proposed. In particular, Feature Point Location (FPL) centroid values, approximated Laplacian matrix values, and Scale determinant values are used to identify artificial matured mango fruit. The results of the experiment are conducted on two different datasets which demonstrates the efficiency of the method proposed.

Keywords: Artificially matured, speeded-up robust features (SURF), feature point location (FPL) centroid, Laplacian matrix, scale determinant value, machine vision