Machine vision extracted plant movement for early detection of plant water stress

Murat Kacira, Peter P. Ling, Ted H. Short

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


A methodology was established for early, non-contact, and quantitative detection of plant water stress with machine vision extracted plant features. Top-projected canopy area (TPCA) of the plants was extracted from plant images using image-processing techniques. Water stress induced plant movement was decoupled from plant diurnal movement and plant growth using coefficient of relative variation of TPCA (CRVTPCA) and was found to be an effective marker for water stress detection. Threshold value of CRVTPCA as an indicator of water stress was determined by a parametric approach. The effectiveness of the sensing technique was evaluated against the timing of stress detection by an operator. Results of this study suggested that plant water stress detection using projected canopy area based features of the plants was feasible.

Original languageEnglish (US)
Pages (from-to)1147-1153
Number of pages7
JournalTransactions of the American Society of Agricultural Engineers
Issue number4
StatePublished - Jul 2002


  • Image processing
  • Irrigation
  • Machine vision
  • Plant movement
  • Water stress

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)


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