Distance Independent Weight Estimation of Eggs from Images Using Artificial Neural Networks

Abstract

Accurately and fast detection of weight of objects has an important place for lots of academic and industrial application at the present time. In this work, it was aimed to estimate weight of eggs in a distance independent manner using image processing and artificial neural networks (ANN). The constituted system consists of a camera, artificial lighting system, reflector and reference image. Object weight was estimated by processing the acquired image. Feed forward multilayer perceptron (MLP) was preferred as artificial neural network. 250 samples which consist of 4 different brands and 4 different classes (x-large, large, medium, and small) were used for testing and training the system. It was tried to be obtained optimum system by using different feature vectors, different images and different artificial neural network parameters. As accurate classification rate was %47 according to defined by Turkish food codex egg communique and classification values write on the egg cartoon, this rate was increased to %90.5 as a result of this work. All processes were realized by using MATLAB program and toolboxes.