Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Machine Vision Technique for Rapid Measurement of Soybean Seed Vigor

  • Lee, Hoonsoo (USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center) ;
  • Huy, Tran Quoc (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Park, Eunsoo (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Bae, Hyung-Jin (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Baek, Insuck (USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center) ;
  • Kim, Moon S. (USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center) ;
  • Mo, Changyeun (National Academy of Agricultural Science, Rural Development Administration) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • Received : 2017.08.20
  • Accepted : 2017.08.28
  • Published : 2017.09.01
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Abstract

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.

Keywords

References

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Cited by

  1. Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis vol.19, pp.2, 2019, https://doi.org/10.3390/s19020271