Title: Combined RGB colour and local binary pattern statistics features-based classification and identification of vegetable images

Authors: Manohar Madgi; Ajit Danti; Basavaraj Anami

Addresses: K.L.E. Institute of Technology, Hubballi 580030, Karnataka, India ' J.N.N. College of Engineering, Shivamogga 577204, Karnataka, India ' K.L.E. Institute of Technology, Hubballi 580030, Karnataka, India

Abstract: This paper presents a method for classification of vegetables based on RGB colour and local binary pattern (LBP) texture features. The feature vector comprises of the combination of colour and texture features that contribute to the classification. Leafy and non-leafy vegetable images are deployed. In this work 18 varieties of vegetables are considered by choosing nine leafy and nine non-leafy vegetables. A multilayer neural network is used for the classification. The experimental results demonstrated that, with neural networks classifier an overall classification accuracy of 93.3% is achieved across different vegetables. The work finds useful in developing recognition system for super market, packing and grading of vegetable, food processing and Agriculture Produce Market Committee (APMC).

Keywords: RGB colour features; LBP features; vegetable classification; neural networks; recognition systems; local binary pattern; vegetable identification; vegetable images; texture features.

DOI: 10.1504/IJAPR.2015.075947

International Journal of Applied Pattern Recognition, 2015 Vol.2 No.4, pp.340 - 352

Received: 14 Feb 2015
Accepted: 03 May 2015
Published online: 18 Apr 2016 *

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