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Machine vision based alternative testing approach for physical purity, viability and vigour testing of soybean seeds (Glycine max)

  • Shveta Mahajan
  • Sudesh Kumar Mittal
  • Amitava Das
Original Article

Abstract

The conventional methods for seed quality testing have several limitations as they involve visual assessment and are destructive. In this context, a study was performed to assess the suitability of non-contact, non-destructive type imaging techniques such as visible imaging and X-ray imaging for conducting physical purity, viability and vigour tests of soybean seeds. The seeds that appeared healthy in external surface examination using visible tests as well as in internal assessment using X-ray tests were classified as sound seeds while the other seeds were marked as not-sound seeds. The obtained results were then correlated with the results of the standard germination tests. The high correlation results between the imaging tests and the standard conventional germination tests indicate the effectiveness and usability of the proposed image analysis based technique as an attractive alternative to the existing quality assessment methods for soybean seeds.

Keywords

Visible and X-ray imaging Soybean seeds Vigour Viability Physical purity 

Notes

Acknowledgements

The authors would like to express sincere gratitude to Director, CSIR-CSIO for providing infrastructural facilities. The authors would like to acknowledge Punjab Agricultural University (PAU), Ludhiana, India (Dr B. S. Gill: for providing soybean seeds of SL525 variety and Director Seeds: for providing assistance to conduct the standard germination tests at their laboratory). One of the author, Shveta Mahajan, acknowledges the grant of SRF-GATE fellowship from Council of Scientific and Industrial Research (CSIR), New Delhi. This study was supported in part by CSIR-CSIO under the network project ASHA, Task 1.4.

References

  1. Basra AS (1995) Seed quality: basic mechanisms and agricultural implications. Food Products Press, BinghamtonGoogle Scholar
  2. Behtari B, De Luis M, Nasab ADM (2014) Predicting germination of Medicago sativa and Onobrychis viciifolia seeds by using image analysis. Turk J Agric For 38:615–623CrossRefGoogle Scholar
  3. Brosnan T, Sun D-W (2002) Inspection and grading of agricultural and food products by computer vision systems—a review. Comput Electron Agric 36:193–213CrossRefGoogle Scholar
  4. Brosnan T, Sun D-W (2004) Improving quality inspection of food products by computer vision—a review. J Food Eng 61:3–16CrossRefGoogle Scholar
  5. Chen Y-R, Chao K, Kim MS (2002) Machine vision technology for agricultural applications. Comput Electron Agric 36:173–191CrossRefGoogle Scholar
  6. De Carvalho MLM, Van Aelst AC, Van Eck JW, Hoekstra FA (1999) Pre-harvest stress cracks in maize (Zea mays L.) kernels as characterized by visual, X-ray and low temperature scanning electron microscopical analysis: effect on kernel quality. Seed Sci Res 9:227–236Google Scholar
  7. Dell’Aquila A (2006) Red–Green–Blue (RGB) colour density as a non-destructive marker in sorting deteriorated lentil (Lens culinaris Medik.) seeds. Seed Sci Technol 34:609–619CrossRefGoogle Scholar
  8. Dell’Aquila A (2007) Towards new computer imaging techniques applied to seed quality testing and sorting. Seed Sci Technol 35:519–538CrossRefGoogle Scholar
  9. Dell’Aquila A (2009) Digital imaging information technology applied to seed germination testing: a review. In: Lichtfouse E, Navarrete M, Debaeke P, Véronique S, Alberola C (eds) Sustainable agriculture. Springer, Dordrecht, pp 377–388CrossRefGoogle Scholar
  10. Demir I, Mavi K (2008) Controlled deterioration and accelerated aging tests to estimate the relative storage potential of cucurbit seed lots. HortScience 43:1544–1548Google Scholar
  11. Du C-J, Sun D-W (2004) Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15:230–249CrossRefGoogle Scholar
  12. Du C-J, Sun D-W (2006) Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng 72:39–55CrossRefGoogle Scholar
  13. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874CrossRefGoogle Scholar
  14. Gabbouj M, Cheikh FA (1996) Vector median-vector directional hybrid filter for color image restoration. In: Proceedings of the 1996 European signal processing conference, EUSIPCO 1996, Trieste, ItalyGoogle Scholar
  15. Geneve RL, Dutt M (2008) Using sequential digital images to study seed germination. Propag Ornam Plants 8:13–16Google Scholar
  16. Gonzalez RC, Eddins SL, Woods RE (2010) Morphological reconstruction. Digital image processing using MATLAB, MathWorksGoogle Scholar
  17. Guide MUs (1998) The mathworks, vol 5. MATLAB Inc, Natick, p 333Google Scholar
  18. Gunasekaran S (1996) Computer vision technology for food quality assurance. Trends Food Sci Technol 7:245–256CrossRefGoogle Scholar
  19. Gunasekaran S, Cooper T, Berlage A (1988) Evaluating quality factors of corn and soybeans using a computer vision system. Trans ASAE 31:1264–1271CrossRefGoogle Scholar
  20. ISTA (2002) ISTA rules. International Seed Testing Association, ZurichGoogle Scholar
  21. Kannur A, Kannur A, Rajpurohit VS (2011) Classification and grading of bulk seeds using Artificial Neural Network. J Comput Appl 3:62–73Google Scholar
  22. Kilic K, Boyaci IH, Koksel H, Kusmenoglu I (2007) A classification system for beans using computer vision system and artificial neural networks. J Food Eng 78:897–904CrossRefGoogle Scholar
  23. Kotwaliwale N, Singh K, Kalne A, Jha SN, Seth N, Kar A (2011) X-ray imaging methods for internal quality evaluation of agricultural produce. J Food Sci Technol 51:1–15.  https://doi.org/10.1007/s13197-011-0485-y CrossRefGoogle Scholar
  24. Kuensting H, Ogawa Y, Sugiyama J (2002) Structural details in soybeans: a new three-dimensional visualization method. J Food Sci 67:721–724CrossRefGoogle Scholar
  25. Long TP, Kersten H (1936) Stimulation of growth of soy bean seeds by soft X-rays. Plant Physiol 11:615CrossRefGoogle Scholar
  26. Mahajan S, Das A, Sardana HK (2015) Image acquisition techniques for assessment of legume quality. Trends Food Sci Technol 42:116–133CrossRefGoogle Scholar
  27. Mahajan S, Rani A, Sharma M, Mittal SK, Das A (2017) A pre-processing based optimized edge weighting method for colour constancy. Imag Sci J doi:doi.  https://doi.org/10.1080/13682199.2017.1412889 Google Scholar
  28. Mavi K, Demir I (2007a) Controlled deterioration and accelerated ageing tests to predict seedling emergence of watermelon under stressful conditions and seed longevity. Seed Sci Technol 35:445–459CrossRefGoogle Scholar
  29. Mavi K, Demir I (2007b) Controlled deterioration and accelerated aging tests predict relative seedling emergence potential of melon seed lots. HortScience 42:1431–1435Google Scholar
  30. Pinto T, Cicero S, Franca-Neto J, Forti V (2009) An assessment of mechanical and stink bug damage in soybean seed using X-ray analysis test. Seed Sci Technol 37:110–120CrossRefGoogle Scholar
  31. Pinto TLF, Mondo VHV, Gomes-Júnior FG, Cicero SM (2012) Image analysis for evaluating mechanical damages in soybean seeds. Pesquisa Agropecuária Tropical 42:310–316CrossRefGoogle Scholar
  32. Powers DM (2011) Evaluation: from precision, recall and F-measure 703 to ROC, informedness, markedness and correlation. J Mach Learn Technol 2:37–63Google Scholar
  33. Ramakrishnan N, Babu BS, Babu TR (2012) Standardization of X-ray radiography methodology for the detection of hidden infestation in pulses. Indian J Plant Prot 40:12–18Google Scholar
  34. Shahin MA, Symons SJ (2001) A machine vision system for grading lentils. Can Biosyst Eng 43:7Google Scholar
  35. Tiwari RK (2012) Soybeans grading and marking rules, (Subsection (i) vide notification number G.S.R. 41(E)). Gazette of India, ExtraordinaryGoogle Scholar
  36. Vance CP (2001) Symbiotic nitrogen fixation and phosphorus acquisition. Plant nutrition in a world of declining renewable resources. Plant Physiol 127:390–397CrossRefGoogle Scholar

Copyright information

© Association of Food Scientists & Technologists (India) 2018

Authors and Affiliations

  1. 1.Academy of Scientific and Innovative Research (AcSIR)ChandigarhIndia
  2. 2.Computational InstrumentationCSIR-Central Scientific Instruments OrganisationChandigarhIndia
  3. 3.Computer Science and Engineering DepartmentRayat Bahra UniversityMohaliIndia

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