A method with neural networks for the classification of fruits and vegetables

In this paper, a novel method for the classification of fruits and vegetables is introduced. This technique is divided into two parts, the electronic nose and classification method. First, an electronic nose is designed with an arduino microcontroller and with some electronic sensors to obtain real data of the smells of fruits or vegetables. Second, a classification method is introduced with a neural network to detect between three kinds of objects: fruits or vegetables. The introduced strategy is validated by three experiments with the adaline, multilayer, and radial basis function neural networks.

[1]  Walmir M. Caminhas,et al.  A fast learning algorithm for evolving neo-fuzzy neuron , 2014, Appl. Soft Comput..

[2]  Plamen P. Angelov,et al.  Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier , 2015, Neurocomputing.

[3]  Carlos Fernandez-Lozano,et al.  Texture classification using feature selection and kernel-based techniques , 2015, Soft Computing.

[4]  Edwin Lughofer,et al.  Reliable All-Pairs Evolving Fuzzy Classifiers , 2013, IEEE Transactions on Fuzzy Systems.

[5]  Mahardhika Pratama,et al.  pClass: An Effective Classifier for Streaming Examples , 2015, IEEE Transactions on Fuzzy Systems.

[6]  Edwin Lughofer,et al.  Self-adaptive and local strategies for a smooth treatment of drifts in data streams , 2014, Evol. Syst..

[7]  X. Rosalind Wang,et al.  Human breath-print identification by E-nose, using information-theoretic feature selection prior to classification , 2015 .

[8]  Lei Zhang,et al.  A novel sensor selection using pattern recognition in electronic nose , 2014 .

[9]  Michel Ferreira,et al.  Time-evolving O-D matrix estimation using high-speed GPS data streams , 2016, Expert Syst. Appl..

[10]  Bipan Tudu,et al.  Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach , 2014 .

[11]  Francesco Palmieri,et al.  Energy efficiency of elastic frequency grids in multilayer IP/MPLS-over-flexgrid networks , 2015, J. Netw. Comput. Appl..

[12]  Francesco Palmieri,et al.  On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines , 2013, Appl. Soft Comput..

[13]  Moamar Sayed Mouchaweh,et al.  Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: Application to wind turbine converters , 2016, Neurocomputing.

[14]  K. Manimala,et al.  A novel data selection technique using fuzzy C-means clustering to enhance SVM-based power quality classification , 2014, Soft Computing.

[15]  Mohamed Medhat Gaber,et al.  Knowledge discovery from data streams , 2009, IDA 2009.

[16]  Bartosz Krawczyk,et al.  One-class classifiers with incremental learning and forgetting for data streams with concept drift , 2015, Soft Comput..

[17]  Araceli Sanchis,et al.  Evolving classification of UNIX users’ behaviors , 2014, Evol. Syst..

[18]  Royston Goodacre,et al.  A comparison of different chemometrics approaches for the robust classification of electronic nose data , 2014, Analytical and Bioanalytical Chemistry.

[19]  Fernando A. C. Gomide,et al.  Enhanced evolving participatory learning fuzzy modeling: an application for asset returns volatility forecasting , 2014, Evol. Syst..

[20]  Cornelio Yáñez-Márquez,et al.  Data Stream Classification Based on the Gamma Classifier , 2015 .

[21]  Alfredo De Santis,et al.  Network anomaly detection with the restricted Boltzmann machine , 2013, Neurocomputing.

[22]  Igor Skrjanc,et al.  Hybrid-fuzzy modeling and identification , 2014, Appl. Soft Comput..

[23]  Abdelhamid Bouchachia,et al.  Multi-resident Activity Recognition Using Incremental Decision Trees , 2014, ICAIS.

[24]  Edwin Lughofer,et al.  Recent advances on evolving intelligent systems and applications , 2014, Evol. Syst..

[25]  Agapito Ledezma,et al.  Intelligent Promotions Recommendation System for Instaprom Platform , 2014, IDEAL.

[26]  Edwin Lughofer,et al.  Hybrid active learning for reducing the annotation effort of operators in classification systems , 2012, Pattern Recognit..

[27]  Edwin Lughofer,et al.  Learning in Non-Stationary Environments , 2012 .

[28]  Edwin Lughofer,et al.  Autonomous data stream clustering implementing split-and-merge concepts - Towards a plug-and-play approach , 2015, Inf. Sci..

[29]  Moamar Sayed Mouchaweh,et al.  Dynamic supervised classification method for online monitoring in non-stationary environments , 2014, Neurocomputing.

[30]  X. Hong,et al.  Authenticating cherry tomato juices—Discussion of different data standardization and fusion approaches based on electronic nose and tongue , 2014 .

[31]  Asif Ekbal,et al.  MODE: multiobjective differential evolution for feature selection and classifier ensemble , 2015, Soft Computing.

[32]  Xiaowei Yang,et al.  A bilateral-truncated-loss based robust support vector machine for classification problems , 2015, Soft Comput..

[33]  Zhiwei Zhu,et al.  Classification of Rice by Combining Electronic Tongue and Nose , 2015, Food Analytical Methods.

[34]  Igor Skrjanc,et al.  Applications, results and future direction (EAIS 12) , 2014, Evol. Syst..

[35]  K. Hayashi,et al.  Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array , 2014 .

[36]  Edwin Lughofer,et al.  Learning in Non-Stationary Environments: Methods and Applications , 2012 .

[37]  Mahardhika Pratama,et al.  Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm , 2015, IEEE Transactions on Fuzzy Systems.

[38]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[39]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..