Analysis of the stochastic excursions of tumbling apples
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Date
1/09/2021
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Abstract
There are strong economic pressures to improve automated inspection of apples. A considerable difficulty, acknowledged in the literature, but not adequately quantified, is the question of the extent to which the surface of apples, tumbling randomly on rollers, is covered by camera views during inspection. This work demonstrates a method to measure the roll, pitch and yaw of tumbling apples by tracking features on the skin between succeeding camera images and then to use the measured data to provide precise statistical descriptions of the tumbling process. The method was tested on an image library of four apple varietals; Eve and Granny Smith, which have mostly uniform skin colour, and Royal Gala and Braeburn which have a variegated skin colour. The images included apples that rotated stem-over-calyx (as the starting position) and apples that rotated equatorially for all varietals. The variegated varietals had many more trackable skin features (1,731–2,065 image pairs) than the mono-coloured varietals (238–859 image pairs) and stem-over-calyx rotation produced more tracking image pairs (723–2,065 image pairs) than equatorial rotation (238–2,041 image pairs), because the stem and calyx provided trackable features. Probability histograms are presented for the normalized incremental rotation in pitch, roll and yaw for each varietal and each direction of initial rotation. Skew-Gaussian distributions are fitted to the probability data to give the mean, standard deviation, skew and mean square error for the pitch, roll and yaw for each of the four varietals in each of two initial orientations (stem-over-calyx and equatorial). These stochastic characterisations can be used in future Monte Carlo simulations to provide precise determination of camera coverage during the inspection of apples tumbling on rollers. This is an important contribution to the field of automated apple inspection.
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Keywords
Apple inspection, Computer vision, Surface coverage, Feature tracking, Stochastic tumbling
Citation
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 188