Environmentally adaptive segmentation algorithm for outdoor image segmentation

Lei F. Tian, David C. Slaughter

Research output: Contribution to journalArticlepeer-review


An environmentally adaptive segmentation algorithm (EASA) was developed for outdoor field plant detection. Based on a partially supervised learning process, the algorithm can learn from environmental conditions in outdoor agricultural fields and build an image segmentation look-up table on-the-fly. Experiments showed that the algorithm can adapt to most daytime conditions in outdoor fields, such as changes in light source temperature and soil type. When compared to a static segmentation technique which was trained under sunny conditions, the EASA improved the image segmentation by correctly classifying 26.9 and 54.3% more object pixels under partially cloudy and overcast conditions, respectively. The improved image segmentation of the EASA technique also allowed up to 32 times more plant cotyledons to be recognized (by leaf morphology) under overcast lighting conditions when compared with a static segmentation 'technique trained-under sunny conditions.

Original languageEnglish (US)
Pages (from-to)153-168
Number of pages16
JournalComputers and Electronics in Agriculture
Issue number3
StatePublished - Dec 1998


  • Computer vision
  • Field plant
  • Outdoor lighting
  • Segmentation

ASJC Scopus subject areas

  • Forestry
  • Agronomy and Crop Science
  • Computer Science Applications
  • Horticulture


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