A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

Zhihai Lu,Shuihua Wang,Huimin Lu,Siyuan Lu,Yujie Li,Yudong Zhang

doi:10.12792/iciae2017.053

Zhihai Lu, Shuihua Wang + Show 4 more

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https://doi.org/10.12792/iciae2017.053

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Abstract

It remains a challenge to classify different categories of fruits because of the similarities of shape, color, and texture among them. We presented a novel approach in order to classify fruits accurately and efficiently based on computer vision techniques. We obtained the coefficients using fractional Fourier transform. The entropies extracted from the coefficients were fed into the classifier as the features. A multilayer perceptron optimized by an improved hybrid genetic algorithm was used as the classifier. The experiment results on 1653 fruit images demonstrated that the proposed method achieved an overall accuracy of 89.59%, which was superior to the state-of-the art approaches. Our method is effective in identifying fruit cagegories.

Highlights

Automatic fruit classification can help in factory production, supermarket selling, fruit-picking robot, etc
There is no practical method for automatic fruit classification
The proposed method achieved the highest accuracy of 89.59%, and fitness-scaling chaotic ABC (FSCABC)-FNN and BBO-FNN came the second and third with the same accuracy of 89.47%, which indicated the superiority of fractional Fourier transform (FRFT) + IHGA-multilayer perceptron (MLP) to other approaches

Summary

Introduction

Automatic fruit classification can help in factory production, supermarket selling, fruit-picking robot, etc. There is no practical method for automatic fruit classification. Pennington (2009) [1] employed clustering algorithm to classify fruits and vegetables. Pholpho (2011) [2] utilized visible spectroscopy to classify non-bruised and bruised longan fruits. The classification models combined the principal component analysis (PCA), partial least square discriminant analysis and soft independent modeling of class analogy. Yang (2012) [3] applied multispectral imaging analysis to the blueberry yield estimation system

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