Fruit Detection from Images and Displaying Its Nutrition Value Using Deep Alex Network

  • B. Divya Shree
  • R. Brunda
  • N. Shobha RaniEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


This paper presents a simple and efficient approach to perform fruit detection and predict nutrition information of the fruits using deep Alex networks (DAN). The datasets employed for analysis are acquired from fruit 360 database of image processing challenges. Fruit categories include apples, berries, banana, grape, papaya, peach, avocado, and multiple flavors of apple. And also, the experimentations are carried out on various other fruit samples collected from multiple Web repositories. The network architecture is as usual comprised of to five convolution layers and three fully connected layers including the max pooling, RELU layers. The input images are assumed to be of dimensions 227 × 227 × 3 with number of filters of 96 of size 11 × 11 × 3 with a stride length of 4. The results of experiment prove that fruit detection using DAN is efficient with an accuracy of about 91% to classify the fruits of about 50 different categories on a single machine of configurations 1 GPU, 8 GB RAM, and octa-core processor.


Deep learning Fruit image classification Nutrition prediction Object recognition Convolution neural networks 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Amrita School of Arts & SciencesAmrita Vishwa VidyapeethamMysuruIndia

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