An FPGA based Efficient Fruit Recognition System Using Minimum Distance Classifier

Abstract

The paper deals with a simple yet effective fruit identification system developed on an FPGA, SPARTAN 3(XC3S200-5PQ208) platform .The fruits under consideration were apple, banana, sapodilla and strawberry. Out of these selected fruits there were four different classes of apples, two different classes of sapodillas and one class each of the other two fruits. A total of 800 color images, 200 images of each fruit of size 64x64 were used for training.

The fruit identification success rate mainly depends on the feature vector and the Classifier used. The 3D feature vector incorporates two first order statistical features and the shape feature. Using the 3D feature vector the MATLAB analysis of The Minimum Distance Classifier (MID) fetched a success rate of 85%.The Verilog coded Hardware platform was developed by burning the COE file of a Test image generated by JAVA ECLIPSE IDE onto the IP core. The MATLAB results were verified using the Hardware Platform.

Keywords: RGB image, feature vector, MID, Verilog, FPGA, IP core, COE file.