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Region-Based Convolutional Networks for End-to-End Detection of Agricultural Mushrooms

  • Alexander J. OlpinEmail author
  • Rozita Dara
  • Deborah Stacey
  • Mohamed Kashkoush
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

Conventional image processing techniques have been applied to the field of agricultural machine vision for the purposes of identifying crops for quality control, weed detection, automated spraying and harvesting. With the recent advancements in computational hardware Region-based Convolutional Networks have met with varying levels of success in the area of object detection and classification. In this study we found that a Region-based Convolutional Neural Network was able to achieve a 92% accuracy rating while a Region-based Fully Convolutional Network was able to achieve an 87% accuracy rating in the area of object detection operating on a newly create agricultural mushroom dataset.

Keywords

Agriculture Convolutional Networks Object detection Machine vision Region-Based Convolutional Neural Network Region-Based Fully Convolutional Networks 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alexander J. Olpin
    • 1
    Email author
  • Rozita Dara
    • 1
  • Deborah Stacey
    • 1
  • Mohamed Kashkoush
    • 1
  1. 1.University of GuelphGuelphCanada

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