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

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Abstract

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.

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References

  • Banerjee(Roy) R, Chattopadhyay P, Tudu B, Bhattacharyya N, Bandyopadhyay R (2014) Artificial flavor perception of black tea using fusion of electronic nose and tongue response: a Bayesian statistical approach. J Food Eng 142:87–93

    Article  Google Scholar 

  • Costa B S Jales, Angelov PP, Guedes LA (2015) Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier. Neurocomputing 150:289–303

    Article  Google Scholar 

  • Fernandez-Lozano C, Seoane JA, Gestal M, Gaunt TR, Dorado J, Campbell C (2015) Texture classification using feature selection and kernel-based techniques. Soft Comput 19:2469–2480

    Article  Google Scholar 

  • Fiore U, Palmieri F, Castiglione A, De Santis A (2013) Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122:13–23

    Article  Google Scholar 

  • Gama J (2010) Knowledge discovery from data streams. Chapman & Hall/CRC, Boca Raton

    Book  MATH  Google Scholar 

  • Gomide F, Lughofer E (2014) Recent advances on evolving intelligent systems and applications. Evol Syst 5:217–218

    Article  Google Scholar 

  • Gromski PS, Correa E, Vaughan AA, Wedge DC, Turner ML, Goodacre R (2014) A comparison of different chemometrics approaches for the robust classification of electronic nose data. Anal Bioanal Chem 406:7581–7590

    Article  Google Scholar 

  • Hartert L, Sayed-Mouchaweh M (2014) Dynamic supervised classification method for online monitoring in non-stationary environments. Neurocomputing 126:118–131

    Article  Google Scholar 

  • Hong X, Wang J, Qiu S (2014) Authenticating cherry tomato juices—discussion of different data standardization and fusion approaches based on electronic nose and tongue. Food Res Int 60:173–179

    Article  Google Scholar 

  • Iglesias JA, Ledezma A, Sanchis A (2014) Evolving classification of UNIX users’ behaviors. Evol Syst 5:231–238

    Article  Google Scholar 

  • Iglesias JA, Skrjanc I (2014) Applications, results and future direction. Evol Syst 5:2014

    Google Scholar 

  • Jha SK, Hayashi K, Yadava RDS (2014) Neural, fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array. Measurement 55:186–195

    Article  Google Scholar 

  • Krawczyk B, Wozniak M (2015) One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Comput 19:3387–3400

    Article  Google Scholar 

  • Lu L, Deng S, Zhu Z, Tian S (2015) Classification of rice by combining electronic tongue and nose. Food Anal Methods 8(8):1893–1902

    Article  Google Scholar 

  • Lughofer E (2012) Hybrid active learning for reducing the annotation effort of operators in classification systems. Pattern Recogn 45:884–896

    Article  Google Scholar 

  • Lughofer E, Buchtala O (2013) Reliable all-pairs evolving fuzzy classifiers. IEEE Trans Fuzzy Syst 21(4):625–641

    Article  Google Scholar 

  • Lughofer E, Sayed-Mouchaweh M (2015) Autonomous data stream clustering implementing split-and-merge concepts—towards a plug-and-play approach. Inf Sci 304:54–79

    Article  Google Scholar 

  • Maciel L, Gomide F, Ballini R (2014) Enhanced evolving participatory learning fuzzy modeling: an application for asset returns volatility forecasting. Evol Syst 5:75–88

    Article  Google Scholar 

  • Manimala K, David IG, Selvi K (2015) A novel data selection technique using fuzzy C-means clustering to enhance SVM-based power quality classification. Soft Comput 19:3123–3144

    Article  Google Scholar 

  • Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14:194–209

    Article  Google Scholar 

  • Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J, Damas L (2016) Time-evolving O–D matrix estimation using high-speed GPS datastreams. Expert Syst Appl 44:275–288

    Article  Google Scholar 

  • Núñez A, Schutter BD, Sáez D, Skrjanc I (2014) Hybrid-fuzzy modeling and identification. Appl Soft Comput 17:67–78

    Article  Google Scholar 

  • Palmieri F, Fiore U, Castiglione A, De Santis A (2013) On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines. Appl Soft Comput 13:615–627

    Article  Google Scholar 

  • Pozo MM, Iglesias JA, Ledezma AI (2014) Intelligent promotions recommendation system for instaprom platform. Lect Notes on Comput Syst 8669:231–238

    Article  Google Scholar 

  • Pratama M, Anavatti SG, Er MJ, Lughofer ED (2015) pClass: an effective classifier for streaming examples. IEEE Trans Fuzzy Syst 23(2):369–386

    Article  Google Scholar 

  • Pratama M, Anavatti SG, Lu J (2015) Recurrent classifier based on an incremental meta-cognitive-based scaffolding algorithm. IEEE Trans Fuzzy Syst. doi:10.1109/TFUZZ.2015.2402683

    Google Scholar 

  • Prossegger M, Bouchachia A (2014) Multi-resident activity recognition using incremental decision trees. Lect Notes Artif Intell 8779:182–191

    Google Scholar 

  • Ricciardi S, Palmieri F, Castiglione A, Careglio D (2015) Energy efficiency of elastic frequency grids in multilayer IP/MPLS-over-flexgrid networks. J Netw Comput Appl 56:41–47

    Article  Google Scholar 

  • Roger-Jang J-S, Sun C-T, Mitzutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Inc., Upper Saddle River, New Jersey. ISBN: 0-13-261066-3

  • Rosalind-Wang X, Lizier JT, Berna AZ, Bravo FG, Trowell SC (2015) Human breath-print identification by E-nose, using information-theoretic feature selection prior to classification. Sens Actuators B Chem 217:165–174

    Article  Google Scholar 

  • Sayed-Mouchaweh M, Lughofer E (2012) Learning in non-stationary environments: methods and applications. Springer, New York

    Book  MATH  Google Scholar 

  • Shaker A, Lughofer E (2014) Self-adaptive and local strategies for a smooth treatment of drifts in data streams. Evol Syst 5:239–257

    Article  Google Scholar 

  • Sikdar UK, Ekbal A, Saha S (2015) MODE: multiobjective differential evolution for feature selection and classifier ensemble. Soft Comput 19:3529–3549

    Article  Google Scholar 

  • Toubakh H, Sayed-Mouchaweh M (2016) Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: application to wind turbine converters. Neurocomputing 171:1496–1516

    Article  Google Scholar 

  • Uriarte-Arcia AV, Lopez-Yañez I, Yañez-Marquez C, Gama J, Camacho-Nieto O (2015) Data stream classification based on the gamma classifier. Math Prob Eng 2015:1–17

    Article  Google Scholar 

  • Yang X, Han L, Li Y, He L (2015) A bilateral-truncated-loss based robust support vector machine for classification problems. Soft Comput 19:2871–2882

    Article  MATH  Google Scholar 

  • Zhang L, Tian F, Pei G (2014) A novel sensor selection using pattern recognition in electronic nose. Measurement 54:31–39

    Article  Google Scholar 

Download references

Acknowledgments

The author thanks the editor and the reviewers for their valuable comments and insightful suggestions, which can help to improve this research significantly. The authors thank the Secretaria de Investigacion y Posgrado, the Comision de Operacion y Fomento de Actividades Academicas, and the Consejo Nacional de Ciencia y Tecnologia for their help in this research.

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Correspondence to José de Jesús Rubio.

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The author declares that they have no conflict of interest.

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Communicated by V. Loia.

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de Jesús Rubio, J. A method with neural networks for the classification of fruits and vegetables. Soft Comput 21, 7207–7220 (2017). https://doi.org/10.1007/s00500-016-2263-2

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  • DOI: https://doi.org/10.1007/s00500-016-2263-2

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