Original paper: Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments

  • Authors:

  • David Story;Murat Kacira;Chieri Kubota;Ali Akoglu;Lingling An

  • Affiliations:

  • Agricultural and Biosystems Engineering, The University of Arizona, 1177 E. Fourth Street, Shantz Building, Room 403, Tucson, AZ 85721, USA;Agricultural and Biosystems Engineering, The University of Arizona, 1177 E. Fourth Street, Shantz Building, Room 403, Tucson, AZ 85721, USA;School of Plant Sciences, The University of Arizona, 1140 E. South Campus Drive, Forbes Building, Room 303, Tucson, AZ 85721, USA;Electrical and Computer Engineering, The University of Arizona, 1230 E. Speedway Blvd., Tucson, AZ 85721, USA;Agricultural and Biosystems Engineering, The University of Arizona, 1177 E. Fourth Street, Shantz Building, Room 403, Tucson, AZ 85721, USA

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2010

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Abstract

Conventional greenhouse environmental conditions are determined by observation. However, destructive or invasive contact measurements are not practical for real-time monitoring and control applications. At the canopy scale, machine vision has the potential to identify emerging stresses and guide sampling for identification of the stressor. A machine vision-guided plant sensing and monitoring system was used to detect calcium deficiency in lettuce crops grown in greenhouse conditions using temporal, color and morphological changes of the plant. The machine vision system consisted of two main components: a robotic camera positioning system and an image processing module. The machine vision system extracted plant features to determine overall plant growth and health status, including top projected canopy area (TPCA) as a morphological feature; red-green-blue (RGB) and hue-saturation-luminance (HSL) values as color features; and entropy, energy, contrast, and homogeneity as textural features. The machine vision-guided system was capable of extracting plant morphological, textural and temporal features autonomously. The methodology developed was capable of identifying calcium-deficient lettuce plants 1 day prior to visual stress detection by human vision. Of the extracted plant features, TPCA, energy, entropy, and homogeneity were the most promising markers for timely detection of calcium deficiency in the lettuce crop studied.