Plant health monitoring with machine vision

Peter P. Ling, Terence P. Russell, Gene A. Giacomelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Spectral and dynamic morphological features were investigated for plant health monitoring using machine vision techniques. The plants were stressed by withholding all nutrient salts. The spectral reflectance of healthy and stressed lettuce leaves (Latuca sativa cv. `Ostinata') was measured to determine at which wavelength(s) a stressed condition would be apparent. The measured wavebands were between 400 and 1000 nm. A reference waveband was utilized to account for photometric variables such as lighting and surface geometry differences during image acquisition. The expansion of the top projected leaf area (TPLA) was found to be an effective feature to identify stressed plants. The nutrient stressed plant was identifiable within two days after nutrients were withheld from a healthy plant. This was determined by a clearly measurable reduction in TPLA expansion.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsGeorge E. Meyer, James A. DeShazer
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages10
ISBN (Print)0819416789
StatePublished - 1995
EventOptics in Agriculture, Forestry, and Biological Processing - Boston, MA, USA
Duration: Nov 2 1994Nov 4 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherOptics in Agriculture, Forestry, and Biological Processing
CityBoston, MA, USA

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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