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Machine vision system for quality inspection of beans

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Abstract

This paper presents a machine vision system (MVS) for visual quality inspection of beans which is composed by a set of software and hardware. The software was built from proposed approaches for segmentation, classification, and defect detection, and the hardware consists of equipment developed with low-cost electromechanical materials. Experiments were conducted in two modes: offline and online. For offline experiments, aimed at evaluating the proposed approaches, we composed a database containing 270 images of samples of beans with different mixtures of skin colors and defects. In the online mode, the beans contained in a batch, for example, a bag of 1 kg, are spilled continuously on the conveyor belt for the MVS to perform the inspection, similar to what occurs in an automated industrial visual inspection process. In the offline experiments, our approaches for segmentation, classification, and defect detection achieved, respectively, the average success rates of 99.6%, 99.6%, and 90.0%. In addition, the results obtained in the online mode demonstrated the robustness and viability of the proposed MVS, since it is capable to analyze an image of 1280 × 720 pixels, spending only 1.5 s, with average successes rates of 98.5%, 97.8%, and 85.0%, respectively, to segment, classify, and detect defects in the grains contained in each analyzed image.

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  1. https://opencv.org/

References

  1. Somavilla M, de Oliveira VR, Storck CR (2011) Centesimal and mineral composition of beans when defrosted in a microwave oven. Discip Sci Saúde 12(1):103–114

    Google Scholar 

  2. Coêlho J (2017) Grain production - beans, corn and soybeans Setorial Notebook ETENE, Fortaleza. [Online]. Available: pp. 1–14. https://www.bnb.gov.br/documents/ 80223/4141162/51_graos.pdf/42dd9e02-f9fe-10 fc-69ff-314f3c89faf8

  3. Aggarwal AK, Mohan R (2010) Aspect ratio analysis using image processing for rice grain quality. Int J Food Eng 6(5):1–14. https://doi.org/10.2202/1556-3758.1788

    Article  Google Scholar 

  4. BMALS (2015) Brazilian Ministry of Agriculture, Livestock and Supply – Projections of agribusiness Brazil 2014/2015 to 2024/2025 long term projections. – BMALS, Brasilia, Brasil, p. 133, Accessed: Apr. 01, 2018. [Online]. Available: http://www.sapc.embrapa.br/arquivos/consorcio/informe_estatistico/Projecoes_Agronegocio_CAFE_Mapa_2015_2025.pdf.

  5. Stegmayer G, Milone DH, Garran S, Burdyn L (2013) Automatic recognition of quarantine citrus diseases. Expert Syst Appl 40(9):3512–3517. https://doi.org/10.1016/j.eswa.2012.12.059

    Article  Google Scholar 

  6. Pesante-Santana JA, Woldstad JC (2000) Quality inspection task in modern manufacturing quality inspection task in modern manufacturing. Ind Manag Syst Eng Fact Public:2260–2263

  7. Kiliç K, Boyaci IH, Köksel H, Küsmenoglu I (2007) A classification system for beans using computer vision system and artificial neural networks. J Food Eng 78(3):897–904. https://doi.org/10.1016/j.jfoodeng.2005.11.030

    Article  Google Scholar 

  8. Patil NK, Yadahalli RM, Pujari J (2011) Comparison between HSV and YCbCr color model color-texture based classification of the food grains. Int J Comput Appl 34(4):51–57

    Google Scholar 

  9. Cabral JDD, de Araújo SA (2015) An intelligent vision system for detecting defects in glass products for packaging and domestic use. Int J Adv Manuf Technol 77(1–4):485–494

    Article  Google Scholar 

  10. Embrapa (2012) Bean classification manual. Embrapa, Brasilia, Brasil, pp. 1–25 [Online]. Available: https://ainfo.cnptia.embrapa.br/digital/bitstream/item/101039/1/ manualilustrado-06.pdf.

  11. Sampaio VAM (2016) Grains classification - step by step. Bahia Farmers and Irrigators Association - Aiba, pp. 1-23. [Online]. Available: https://aiba.org.br/wp-content/uploads/2017/01/Cartilha-Classificacao-de-Graos-Versao-Digital.pdf

  12. Posada J, Toro C, Barandiaran I, Oyarzun D, Stricker D, de Amicis R, Pinto EB, Eisert P, Dollner J, Vallarino I (2015) Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Comput Graph Appl 35(2):26–40

    Article  Google Scholar 

  13. Aguilera J, Cipriano A, Eraña M, Lillo I, Mery D, Soto A (2007) Computer vision for quality control in Latin American food industry, a case study. In: Int. conf. on computer vision (ICCV2007): workshop on computer vision applications for developing countries, pp 1–11

    Google Scholar 

  14. Swati, Chanana R (2014) Grain counting method based on machine vision. Int J Adv Technol Eng Sci 02(08):328–332

    Google Scholar 

  15. Dubosclard P, Larnier S, Konik H, Herbulot A, Devy M (2014) Automatic method for visual grading of seed food products. Lect Notes Comput Sci (ICIAR 2014) 8814:485–495. https://doi.org/10.1007/978-3-319-11758-4_53

    Article  MathSciNet  Google Scholar 

  16. Dubosclard P, Larnier S, Konik H, Herbulot A, Devy M (2015) Deterministic method for automatic visual grading of seed food products. In: 4th Int. Conference on Pattern Recognition Applications and Methods, pp 1–6

    Google Scholar 

  17. Dubosclard P, Larnier S, Konik H, Herbulot A, Devy M (2015) Automated visual grading of grain kernels by machine vision. In: Twelfth International Conference on Quality Control by Artificial Vision, vol 9534, pp 1–8. https://doi.org/10.1117/12.2182793

    Chapter  Google Scholar 

  18. Zareiforoush H, Minaei S, Alizadeh MR, Banakar A, Samani BH (2016) Design, development and performance evaluation of an automatic control system for rice whitening machine based on computer vision and fuzzy logic. Comput Electron Agric 124:14–22. https://doi.org/10.1016/j.compag.2016.01.024

    Article  Google Scholar 

  19. Bhat S, Panat S, Arunachalam N (2017) Classification of rice grain varieties arranged in scattered and heap fashion using image processing. In: Ninth International Conference on Machine Vision (ICMV 2016), vol 10341 Icmv 2016, pp 1–6. https://doi.org/10.1117/12.2268802

    Chapter  Google Scholar 

  20. Ramos PJ, Prieto FA, Montoya EC, Oliveros CE (2017) Automatic fruit count on coffee branches using computer vision. Comput Electron Agric 137:9–22. https://doi.org/10.1016/j.compag.2017.03.010

    Article  Google Scholar 

  21. Arboleda ER, Fajardo AC, Medina RP (2018) Classification of coffee bean species using image processing, artificial neural network and K nearest neighbors. In: Innovative Research and Development (ICIRD), IEEE International Conference on, pp 1–5

    Google Scholar 

  22. Zambrano CEP, Caceres JCG, Ticona JR, Beltrán-Castanón NJ, Cutipa JMR, Beltrán-Castanón CA (2018) An enhanced algorithmic approach for automatic defects detection in green coffee beans. In: 9th International Conference on Pattern Recognition Systems (ICPRS 2018), pp 1–8

    Google Scholar 

  23. Venora G, Grillo O, Ravalli C, Cremonini R (2007) Tuscany beans landraces, on-line identification from seeds inspection by image analysis and Linear Discriminant Analysis. Agrochimica 51(4–5):254–268

    Google Scholar 

  24. Venora G, Grillo O, Ravalli C, Cremonini R (2009) Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system. Sci Hortic (Amsterdam) 121(4):410–418. https://doi.org/10.1016/j.scienta.2009.03.014

    Article  Google Scholar 

  25. Laurent B, Ousman B, Dzudie T, Carl MFM, Emmanuel T (2010) Digital camera images processing of hard-to-cook beans. J Eng Technol Res 2(9):177–188

    Google Scholar 

  26. Araújo SA, Alves WAL, Belan PA, Anselmo KP (2015) A computer vision system for automatic classification of most consumed Brazilian beans. Lect Notes Comput Sci 9475:45–53. https://doi.org/10.1007/978-3-319-27863-6

    Article  Google Scholar 

  27. Belan PA, Araújo SA, Alves WAL (2016) An intelligent vision-based system applied to visual quality inspection of beans. Lect Notes Comput Sci (ICIAR 2016) 9730:801–809. https://doi.org/10.1007/978-3-319-41501-7

    Article  Google Scholar 

  28. Belan PA, De Macedo RAG, Pereira MA, Alves WAL, Araújo SA (2018) A fast and robust approach for touching grains segmentation. Lect Notes Comput Sci (ICIAR 2018) 10882:482–489. https://doi.org/10.1007/978-3-319-93000-8

    Article  Google Scholar 

  29. Liu J, Yang WW, Wang Y, Rababah TM, Walker LT (2011) Optimizing machine vision based applications in agricultural products by artificial neural network. Int J Food Eng 7(3):1–25. https://doi.org/10.2202/1556-3758.1745

    Article  Google Scholar 

  30. Siddagangappa MR, Kulkarni AH (2014) Classification and quality analysis of food grains. J Comput Eng 16(4):01–10

    Google Scholar 

  31. Nasirahmadi A, Behroozi-Khazaei N (2013) Identification of bean varieties according to color features using artificial neural network. Span J Agric Res 11(3):670–677. https://doi.org/10.5424/sjar/2013113-3942

    Article  Google Scholar 

  32. Alban N, Laurent B, Martin Y, Ousman B (2014) Quality inspection of bag packaging red beans (Phaseolus vulgaris) using fuzzy clustering algorithm. Br J Math Comput Sci 4(24):3369–3386. https://doi.org/10.9734/bjmcs/2014/12981

    Article  Google Scholar 

  33. Araújo SA, Pessota JH, Kim HY (2015) Beans quality inspection using correlation-based granulometry. Eng Appl Artif Intell 40:84–94. https://doi.org/10.1016/j.engappai.2015.01.004

    Article  Google Scholar 

  34. Alves, WAL, Hashimoto, RF (2014) Ultimate grain filter. In: IEEE International Conference on Image Processing (ICIP), pp 2953–2957. https://doi.org/10.1109/ICIP.2014.7025597

  35. Garcia M, Trujillo M, Chaves D (2017) Global and local features for bean image classification. In: 7th Latin American Conference on Networked and Electronic Media (LACNEM 2017), pp 45–50. https://doi.org/10.1049/ic.2017.0034

    Chapter  Google Scholar 

  36. OuYang A, Gao R, Liu Y, Dong X (2010) An automatic method for identifying different variety of rice seeds using machine vision technology. In: 2010 Sixth International Conference on Natural Computation, vol 1, pp 84–88

    Chapter  Google Scholar 

  37. Pearson T (2009) Hardware-based image processing for high-speed inspection of grains. Comput Electron Agric 69(1):12–18

    Article  Google Scholar 

  38. Jayas DS, Singh CB (2012) 15 - Grain quality evaluation by computer vision. In: Sun D-W (ed) Computer vision technology in the food and beverage industries. Woodhead Publishing, Cambridge, UK, pp 400–421. https://doi.org/10.1533/9780857095770.3.400

  39. Soille P (2004) Morphological Image Analysis: Principles and Applications. Springer-Verlag Berlin Heidelberg, New York, 2nd edition, 392p. https://doi.org/10.1007/978-3-662-05088-0

  40. Araújo SA, Kim HY (2011) Ciratefi: an RST-invariant template matching with extension to color images. Integr Comput Aided Eng 18(1):75–90. https://doi.org/10.3233/ICA-2011-0358

    Article  Google Scholar 

  41. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8:179–187. https://doi.org/10.1109/TIT.1962.1057692

    Article  MATH  Google Scholar 

  42. Illingworth J, Kittler J (1987) The Adaptive Hough Transform. IEEE Trans Pattern Anal Mach Intell PAMI-9(5):690–698

    Article  Google Scholar 

  43. Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement. arXiv, [Online]. Available: http://arxiv.org/abs/1804.02767.

  44. Anami BS, Savakar DG (2010) Influence of light, distance and size on recognition and classification of food grains’ images. Int J Food Eng 6(2):1698. https://doi.org/10.2202/1556-3758

    Article  Google Scholar 

  45. Souza TLPO et al (2013) Common bean cultivars from Embrapa and partners available for 2013. Available: http://ainfo.cnptia.embrapa.br/digital/ bitstream/item/97404 /1/comunicadotecnico-211.pdf (accessed Mar. 01, 2018).

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Funding

This work was supported by the FAPESP–São Paulo Research Foundation (Proc. 2017/05188-9), and by the CNPq–Brazilian National Research Council (research scholarship granted to S. A. Araújo, Proc. 313765/2019-7).

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Authors and Affiliations

  1. Informatics and Knowledge Management Post Graduate Program, Nove de Julho University - UNINOVE, Rua Vergueiro, 235/249, Liberdade, São Paulo, SP, 01504-001, Brazil

    Peterson Adriano Belan, Robson Aparecido Gomes de Macedo, Wonder Alexandre Luz Alves & Sidnei Alves Araújo

  2. Federal University of ABC, Av. dos Estados, 5001, Bangu, Santo André, SP, 09210-580, Brazil

    José Carlos Curvelo Santana

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  1. Peterson Adriano Belan
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  5. Sidnei Alves Araújo
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Correspondence to Sidnei Alves Araújo.

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Belan, P.A., de Macedo, R.A.G., Alves, W.A.L. et al. Machine vision system for quality inspection of beans. Int J Adv Manuf Technol 111, 3421–3435 (2020). https://doi.org/10.1007/s00170-020-06226-5

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