Comparative Analysis of Fruit Categorization Using Different Classifiers

  • Chirag C. PatelEmail author
  • Vimal K. Chaudhari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


The aim of the paper is to measure the performance of fruit images on different mentioned classifiers based on color, zone, area, centroid, size, equvidiameter, perimeter and roundness features. Features are extracted automatically from the provided images and passed to classifiers such as multi-class SVM, KNN, Naive Bayes, random forest and neural network to train the models, classify test images and measure the performance of the classifiers on different data mining tools. The experimental results show that multi-class SVM obtained 87.5 and 91.67% accuracy which is the highest accuracy rather than other mentioned classifiers. KNN has 58.3, 62.5 and 45.83%, Naive Bayes has 62.5 and 58.3%, random forest has 70.8, 75 and 83.33%, and neural network has 66.7 and 75% accuracy based on different data mining tools. Confusion matrix and ROC analysis also show that multi-class SVM has an efficient performance on the proposed features than other mentioned classifiers.


Fruit classification Image classification Image recognition SVM KNN Random forest Naive Bayes Neural network 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.UCCC & SPBCBA & SDHG College of BCA & I.T, UdhnaSuratIndia
  2. 2.Department of Computer ScienceVeer Narmard South Gujarat UniversitySuratIndia

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