Image Processing Methods for Measurement of Lettuce Fresh Weight

초록

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

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