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Sunflower head, disc, and ray florets dimensions measurement using image processing

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1801328.(doi:10.13031/aim.201801328)
Authors:   Sunoj Shajahan, Subhashree Navaneetha Srinivasagan, Dharani Suresh Babu, Igathinathane Cannayen, Jose G Franco, Rachel E Mallinger, Jarrad R Prasifka, David Archer
Keywords:   Algorithm, Dimension measurement, Image processing, Pollinator, Projective transform


Sunflower head, disc, and ray florets dimensions can be correlated to pollinators attraction and seed yields. Dimensions measured manually at present are subjective and time-intensive, therefore, an image processing method was developed as an alternative, which was objective, non-destructive, produces various outputs, and rapid. An ImageJ user-coded plugin with a field image acquisition method was developed to measure the dimensions of individual sunflower floral components, such as head, disc, and ray florets. Two measurement methods, direct (using the thresholded binary image) and wrapping-polygon (using a polygonal enclosure) were tested. The ‘pixel-march‘ method made multiple radial dimension measurements (diameter) on the ray florets binary image in a single computation. The effect of multiple measurements (2, 4, 8, 16, 32, 64, 128, and 180 along 0–180 angles) was studied to determine an effective number of measurements, and user-friendly sunflower dimension prediction models from ImageJ‘s standard output parameters were developed. Results indicated that (i) a minimum of 32 measurements for the sunflower head and ray florets dimensions, but only 8 measurements for the sunflower disc, were necessary; (ii) wrapping-polygon method was efficient compared to direct; (iii) equivalent diameter (ED) was well correlated (r ≥ 0.88) to the accurate mean 180 measurements (D180) for all sunflower components; (iv) linear models for predicting D180 using ED performed better (R2 > 0.99) for head and disc than for ray florets (R2 > 0.78); (v) user-friendly linear models using the mean of two manual measurements of the head (D2h) for predicting D180 and area were good only for the head (R2 > 0.92), and not suitable for disc (R2 ≤ 0.62) and ray florets (R2 ≤ 0.43); and (vi) the developed image processing method was accurate, quick (≈11 s in Windows 10, Intel Core i5, and 8 GB RAM laptop), and has the potential to be adapted to other species and similar objects.

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