Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Image Processing Methods for Measurement of Lettuce Fresh Weight

  • Jung, Dae-Hyun (Department of Biosystems & Biomaterials Science and Engineering, Seoul National University) ;
  • Park, Soo Hyun (Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Han, Xiong Zhe (Department of Biosystems & Biomaterials Science and Engineering, Seoul National University) ;
  • Kim, Hak-Jin (Department of Biosystems & Biomaterials Science and Engineering, Seoul National University)
  • Received : 2015.01.06
  • Accepted : 2015.02.22
  • Published : 2015.03.01
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Abstract

Purpose: Machine vision-based image processing methods can be useful for estimating the fresh weight of plants. This study analyzes the ability of two different image processing methods, i.e., morphological and pixel-value analysis methods, to measure the fresh weight of lettuce grown in a closed hydroponic system. Methods: Polynomial calibration models are developed to relate the number of pixels in images of leaf areas determined by the image processing methods to actual fresh weights of lettuce measured with a digital scale. The study analyzes the ability of the machine vision- based calibration models to predict the fresh weights of lettuce. Results: The coefficients of determination (> 0.93) and standard error of prediction (SEP) values (< 5 g) generated by the two developed models imply that the image processing methods could accurately estimate the fresh weight of each lettuce plant during its growing stage. Conclusions: The results demonstrate that the growing status of a lettuce plant can be estimated using leaf images and regression equations. This shows that a machine vision system installed on a plant growing bed can potentially be used to determine optimal harvest timings for efficient plant growth management.

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