Weed Recognition Framework for Robotic Precision Farming

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11 Citations (Scopus)

Abstract

In this paper, we introduce a novel framework which applies known image features combined with advanced linear image representations for weed recognition. Our proposed weed recognition framework, is based on state-of-the-the art object/image categorization methods exploiting enhanced performance using advanced encoding and machine learning algorithms. The resulting system can be applied in a variety of environments, plantation or weed types. This results in a novel and generic weed control approach, that in our knowledge is unique among weed recognition methods and systems. For the experimental evaluation of our system, we introduce a challenging image dataset for weed recognition. We experimentally show that the proposed system achieves significant performance improvements in weed recognition in comparison with other known methods.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Imaging Systems and Techniques (IST)
Number of pages6
PublisherIEEE
Publication date6 Oct 2016
ISBN (Print)978-1-5090-1818-5
ISBN (Electronic)978-1-5090-1817-8
DOIs
Publication statusPublished - 6 Oct 2016
EventIEEE International Conference on Imaging Systems and Techniques - Chania, Greece
Duration: 4 Oct 20166 Oct 2016
http://ist2016.ieee-ims.org/

Conference

ConferenceIEEE International Conference on Imaging Systems and Techniques
CountryGreece
CityChania
Period04/10/201606/10/2016
Internet address

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