Automated machine vision guided plant monitoring system for greenhouse crop diagnostics

D. Story, M. Kacira

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations


A machine vision guided plant sensing and monitoring system was designed and constructed to autonomously monitor and extract color features (Red-Green- Blue, Hue-Saturation-Luminance, and Color Brightness), textural features (Contrast, Energy, Entropy, and Homogeneity), morphological feature (Top Projected Canopy Area), plant indices (from NIR band relationships with color bands), and plant thermal radiation. A total of 17 features were extracted to monitor the growth and development of lettuce plants growing in a Nutrient Film Technique (NFT) system. From these 17 features, it was found that only 14 features were significant markers to separate the treatment group (induced water stress by withholding nutrient solution from selected rows) from the control. Using these 14 features at a 99% confidence interval and detecting when half of the features are shown to be significant as a threshold for onset of induced stress, it was shown that the system was able to detect the water stress 2 hours before human visual detection.

Original languageEnglish (US)
Title of host publicationInternational Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant Factory - Greensys 2013
PublisherInternational Society for Horticultural Science
Number of pages7
ISBN (Print)9789462610248
StatePublished - 2014

Publication series

NameActa Horticulturae
ISSN (Print)0567-7572


  • Computer vision
  • Crop monitoring
  • Data acquisition
  • Greenhouse
  • Image processing

ASJC Scopus subject areas

  • Horticulture


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