Robust crop and weed segmentation under uncontrolled outdoor illumination

Hong Y. Jeon, Lei F. Tian, Heping Zhu

Research output: Contribution to journalArticle

Abstract

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).

Original languageEnglish (US)
Pages (from-to)6270-6283
Number of pages14
JournalSensors
Volume11
Issue number6
DOIs
StatePublished - Jun 1 2011

Keywords

  • Field crop
  • Machine vision
  • Outdoor illumination
  • Weed identification

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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