Performance analysis of soft computing techniques for the automatic classification of fruits dataset

  • L. RajasekarEmail author
  • D. Sharmila


Different properties of numerous types of fruits and vegetable classification are still an intricate task. The soft computing strategies are used to recognize a fruit by blending the three basic features which characterize the object: color, shape and texture. The classifiers are relatively effective, when the image feature vector is fused with one another. This technique decreases the dimensionality of the feature vector. So the combined and normalized features of the image are producing better classification accuracy with minimum number of training data. K-nearest neighbor (K-NN), linear discriminant analysis, naive Bayes, error-correcting output classifier and decision tree classifiers are used for image recognition process. A tenfold cross-validation technique is used to improve the classification accuracy of the classifier. The experiment is demonstrated in all the five techniques with 2400 images from the 24 categories of fruits and vegetables. The K-NN scored 97.5% of classification accuracy.


K-nearest neighbor Naives Bayes Decision tree Classification Fruits dataset 


Compliance with ethical standard

Conflict of interest

The authors declare that they have no conflicts of interest.

Research involving human participants and/or animals

This article does not contain any studies with human participants performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringBannari Amman Institute of TechnologySathyamangalamIndia
  2. 2.Department of Information TechnologyDr. N.G.P. Institute of TechnologyCoimbatoreIndia

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