Soft Computing

, Volume 21, Issue 23, pp 7207–7220 | Cite as

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

Methodologies and Application

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.

Keywords

Adaline neural networks Multilayer neural networks Radial basis function neural networks Classification Electronic nose Fruits Vegetables 

References

  1. 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 142:87–93CrossRefGoogle Scholar
  2. 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 150:289–303CrossRefGoogle Scholar
  3. 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 19:2469–2480CrossRefGoogle Scholar
  4. Fiore U, Palmieri F, Castiglione A, De Santis A (2013) Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122:13–23CrossRefGoogle Scholar
  5. Gama J (2010) Knowledge discovery from data streams. Chapman & Hall/CRC, Boca RatonCrossRefMATHGoogle Scholar
  6. Gomide F, Lughofer E (2014) Recent advances on evolving intelligent systems and applications. Evol Syst 5:217–218CrossRefGoogle Scholar
  7. 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 406:7581–7590CrossRefGoogle Scholar
  8. Hartert L, Sayed-Mouchaweh M (2014) Dynamic supervised classification method for online monitoring in non-stationary environments. Neurocomputing 126:118–131CrossRefGoogle Scholar
  9. 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 60:173–179CrossRefGoogle Scholar
  10. Iglesias JA, Ledezma A, Sanchis A (2014) Evolving classification of UNIX users’ behaviors. Evol Syst 5:231–238CrossRefGoogle Scholar
  11. Iglesias JA, Skrjanc I (2014) Applications, results and future direction. Evol Syst 5:2014Google Scholar
  12. 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 55:186–195CrossRefGoogle Scholar
  13. Krawczyk B, Wozniak M (2015) One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Comput 19:3387–3400CrossRefGoogle Scholar
  14. Lu L, Deng S, Zhu Z, Tian S (2015) Classification of rice by combining electronic tongue and nose. Food Anal Methods 8(8):1893–1902CrossRefGoogle Scholar
  15. Lughofer E (2012) Hybrid active learning for reducing the annotation effort of operators in classification systems. Pattern Recogn 45:884–896CrossRefGoogle Scholar
  16. Lughofer E, Buchtala O (2013) Reliable all-pairs evolving fuzzy classifiers. IEEE Trans Fuzzy Syst 21(4):625–641CrossRefGoogle Scholar
  17. Lughofer E, Sayed-Mouchaweh M (2015) Autonomous data stream clustering implementing split-and-merge concepts—towards a plug-and-play approach. Inf Sci 304:54–79CrossRefGoogle Scholar
  18. Maciel L, Gomide F, Ballini R (2014) Enhanced evolving participatory learning fuzzy modeling: an application for asset returns volatility forecasting. Evol Syst 5:75–88CrossRefGoogle Scholar
  19. 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 19:3123–3144CrossRefGoogle Scholar
  20. Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14:194–209CrossRefGoogle Scholar
  21. 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 44:275–288CrossRefGoogle Scholar
  22. Núñez A, Schutter BD, Sáez D, Skrjanc I (2014) Hybrid-fuzzy modeling and identification. Appl Soft Comput 17:67–78CrossRefGoogle Scholar
  23. 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 13:615–627CrossRefGoogle Scholar
  24. Pozo MM, Iglesias JA, Ledezma AI (2014) Intelligent promotions recommendation system for instaprom platform. Lect Notes on Comput Syst 8669:231–238CrossRefGoogle Scholar
  25. Pratama M, Anavatti SG, Er MJ, Lughofer ED (2015) pClass: an effective classifier for streaming examples. IEEE Trans Fuzzy Syst 23(2):369–386CrossRefGoogle Scholar
  26. 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
  27. Prossegger M, Bouchachia A (2014) Multi-resident activity recognition using incremental decision trees. Lect Notes Artif Intell 8779:182–191Google Scholar
  28. 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 56:41–47CrossRefGoogle Scholar
  29. 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: 0-13-261066-3Google Scholar
  30. 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 217:165–174CrossRefGoogle Scholar
  31. Sayed-Mouchaweh M, Lughofer E (2012) Learning in non-stationary environments: methods and applications. Springer, New YorkCrossRefMATHGoogle Scholar
  32. Shaker A, Lughofer E (2014) Self-adaptive and local strategies for a smooth treatment of drifts in data streams. Evol Syst 5:239–257CrossRefGoogle Scholar
  33. Sikdar UK, Ekbal A, Saha S (2015) MODE: multiobjective differential evolution for feature selection and classifier ensemble. Soft Comput 19:3529–3549CrossRefGoogle Scholar
  34. 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 171:1496–1516CrossRefGoogle Scholar
  35. 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 2015:1–17CrossRefGoogle Scholar
  36. Yang X, Han L, Li Y, He L (2015) A bilateral-truncated-loss based robust support vector machine for classification problems. Soft Comput 19:2871–2882CrossRefMATHGoogle Scholar
  37. Zhang L, Tian F, Pei G (2014) A novel sensor selection using pattern recognition in electronic nose. Measurement 54:31–39CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Sección de Estudios de Posgrado e Investigación, ESIME AzcapotzalcoInstituto Politécnico NacionalMexicoMexico

Personalised recommendations