Plant Water Stress Detection Using Machine Vision Extracted Plant Movement

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

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

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 variation of TPCA (COV TPCA) and was found to be effective for the water stress detection. Threshold value of COV TPCA 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 a grower. Results of this study suggested that the objective water stress detection using projected canopy area based feature of the plants was feasible.

Original languageEnglish (US)
Pages239-252
Number of pages14
StatePublished - Dec 1 2000
Externally publishedYes
Event2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century - Milwaukee, WI., United States
Duration: Jul 9 2000Jul 12 2000

Other

Other2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century
CountryUnited States
CityMilwaukee, WI.
Period7/9/007/12/00

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Kacira, M., Ling, P. P., & Short, T. H. (2000). Plant Water Stress Detection Using Machine Vision Extracted Plant Movement. 239-252. Paper presented at 2000 ASAE Annual International Meeting, Technical Papers: Engineering Solutions for a New Century, Milwaukee, WI., United States.