DeepFruits: A Fruit Detection System Using Deep Neural Networks

  title={DeepFruits: A Fruit Detection System Using Deep Neural Networks},
  author={Inkyu Sa and ZongYuan Ge and Feras Dayoub and Ben Upcroft and Tristan Perez and Chris McCool},
This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through… CONTINUE READING
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Open datasets and tutorial documentation, 2016

  • Z. Y. Ge, I. Sa
  • Available online: (accessed…
  • 2016
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3 Excerpts

Convolutional Neural Networks for Visual Recognition (2016)

  • Stanford University. CS231n
  • Available online:…
  • 2016
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