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Tropical Fruits Classification Using an AlexNet-Type Convolutional Neural Network and Image Augmentation

  • Alberto Patino-Saucedo
  • Horacio Rostro-Gonzalez
  • Jorg Conradt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

AlexNet is a Convolutional Neural Network (CNN) and reference in the field of Machine Learning for Deep Learning. It has been successfully applied to image classification, especially in large sets such as ImageNet. Here, we have successfully applied a smaller version of the AlexNet CNN to classify tropical fruits from the Supermarket Produce dataset. This database contains 2633 images of fruits divided into 15 categories with high variability and complexity, i.e. shadows, pose, occlusion, reflection (fruits inside a bag), etc. Since few training samples are required for fruit classification and to prevent overfitting, the modified AlexNet CNN has fewer feature maps and fully connected neurons than the original one, and data augmentation of the training set is used. Numerical results show a top-1 classification accuracy of 99.56 %, and a top-2 accuracy of 100 % for the 15 classes, which outperforms previous works on the same dataset.

Keywords

Convolutional neural networks AlexNet CNN Fruit classification Image augmentation 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alberto Patino-Saucedo
    • 1
  • Horacio Rostro-Gonzalez
    • 1
  • Jorg Conradt
    • 2
    • 3
  1. 1.Department of ElectronicsUniversity of GuanajuatoSalamancaMexico
  2. 2.Department of Electrical and Computer Engineering, Neuroscientific System TheoryTechnical University of MunichMunichGermany
  3. 3.School of Electrical Engineering and Computer ScienceKTHStockholmSweden

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