Estimation of plant species by classifying plants and leaves in combination

Mads Dyrmann, Peter Christiansen, Henrik Skov Midtiby

Research output: Contribution to journalJournal articleResearchpeer-review


Information on which weed species are present within agricultural fields is a prerequisite when using robots for site‐specific weed management. This study proposes a method of improving robustness in shape‐based classifying of seedlings toward natural shape variations within each plant species. To do so, leaves are separated from plants and classified individually together with the classification of the whole plant. The classification is based on common, rotation‐invariant features. Based on previous classifications of leaves and plants, confidence in correct assignment is created for the plants and leaves, and this confidence is used to determine the species of the plant. By using this approach, the classification accuracy of eight plants species at early growth stages is increased from 93.9% to 96.3%.
Original languageEnglish
JournalJournal of Field Robotics
Issue number2
Pages (from-to)202-212
Publication statusPublished - 1. Mar 2018



  • Bayes belief integration
  • automated weed control
  • classifier fusion
  • computer vision
  • excessive green
  • phenotyping
  • plant classification

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