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Crop and weed detection using image processing and deep learning techniques
University of Skövde, School of Engineering Science.
University of Skövde, School of Engineering Science.
2020 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Artificial intelligence, specifically deep learning, is a fast-growing research field today. One of its various applications is object recognition, making use of computer vision. The combination of these two technologies leads to the purpose of this thesis. In this project, a system for the identification of different crops and weeds has been developed as an alternative to the system present on the FarmBot company’s robots. This is done by accessing the images through the FarmBot API, using computer vision for image processing, and artificial intelligence for the application of transfer learning to a RCNN that performs the plants identification autonomously. The results obtained show that the system works with an accuracy of 78.10% for the main crop and 53.12% and 44.76% for the two weeds considered. Moreover, the coordinates of the weeds are also given as results. The performance of the resulting system is compared both with similar projects found during research, and with the current version of the FarmBot weed detector. Form a technological perspective, this study presents an alternative to traditional weed detectors in agriculture and open the doors to more intelligent and advanced systems.

Place, publisher, year, edition, pages
2020. , p. 76
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:his:diva-18630OAI: oai:DiVA.org:his-18630DiVA, id: diva2:1447357
External cooperation
Naturbruksförvaltningen Sötåsen
Subject / course
Industrial Engineering
Supervisors
Examiners
Available from: 2020-06-25 Created: 2020-06-25 Last updated: 2020-06-25Bibliographically approved

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Production Engineering, Human Work Science and Ergonomics

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf