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Image-based automated potato tuber shape evaluation

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Abstract

Potato tuber length to width (L/W) ratio is an indicator of shape phenotype, which is an important quality trait assessed in breeding and variety development. The standard method of measurement using calipers is labor intensive and time consuming. In this study, an image acquisition system was integrated with an automated potato sizer to capture video data of tubers during sorting for estimation of L/W ratios. An algorithm was developed to segment and estimate the L/W ratios from the video frame in an accurate and high-throughput manner. Line profile was used to determine the tuber position in the frame. The minimal bounding rectangle of each tuber was computed to estimate length and width of the tubers. The imaging conditions (light, imaging distance, and speed) were optimized using fresh market potato tubers (43 samples). Finally, the algorithm was tested with eight sets of field samples of tubers of cultivars Bondi and Alturas (about 709–1273 samples/set). Optimization results indicated that L/W measurement accuracy was higher than 95% for the fresh market potato tubers, with no significant effect of tested imaging conditions. There was also a significant correlation between ground-truth caliper measurements and image-based data (Pearson’s correlation coefficient: 0.84–0.99, p < 0.01). The accuracies of L/W estimations for field samples of Bondi and Alturas tubers ranged from 76 to 100%. The lower accuracies are likely attributed to differences in sample size. Nevertheless, the method is applicable for rapid and accurate estimation of L/W ratio for a large set of samples .

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Acknowledgements

This research was funded, in part, by the USDA National Institute of Food and Agriculture, Hatch Project, 1002864 (WNP00821) and by the program of study abroad for young teachers sponsored by the Agricultural University of Hebei.

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Authors and Affiliations

  1. College of Information Science and Technology, Hebei Agricultural University, Baoding, Hebei, China

    Yongsheng Si

  2. Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA

    Yongsheng Si & Sindhuja Sankaran

  3. Department of Horticulture, Washington State University, Pullman, WA, USA

    N. Richard Knowles & Mark J. Pavek

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  1. Yongsheng Si
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  2. Sindhuja Sankaran
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  3. N. Richard Knowles
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  4. Mark J. Pavek
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Correspondence to Sindhuja Sankaran.

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Si, Y., Sankaran, S., Knowles, N.R. et al. Image-based automated potato tuber shape evaluation. Food Measure 12, 702–709 (2018). https://doi.org/10.1007/s11694-017-9683-2

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  • DOI: https://doi.org/10.1007/s11694-017-9683-2

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