Skip to main content
SpringerLink
Log in

Texture and Color-Based Analysis to Determine the Quality of the Manila Mango Using Digital Image Processing Techniques

  • Conference paper
  • First Online:
Technologies and Innovation (CITI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1658))

Included in the following conference series:

Abstract

This work shows the development of an algorithm based on image processing techniques and aims to determine the quality of Manila mango for export purposes. Currently in Mexico, in most of the mango producing states, the analysis of this fruit is done manually and the quality of the mango is determined by considering the state of maturity, cleanliness of the mango´s skin and size. During this process, the fruit is granding and then classified; in these tasks, human errors can occur due to fatigue. The consequences of these errors translate into losses for farmers, since for one fruit detected to be of poor quality, the entire lot is rejected. Therefore, any attempt made to support the determination of the quality of this fruit in an automated way will be of great support, especially for small and medium-sized agricultural enterprises (SMEs). The methodology employed uses digital image processing techniques by analyzing color and texture, calculating the mean of the components and using statistical methods (histograms and co-occurrence matrices) in the regions of interest, in addition to applying the support vector machine (SVM) algorithm to classify Manila mango based on maturity and peel damage. The algorithm presented here details a process of obtaining the image to filter it and identify the edges of the mango, the representation and manipulation through the histogram is used to improve the image without affecting aspects that may be relevant in the image such as contours, textures and intensity. These characteristics will be used later to determine the quality of the fruit. The results shown so far are satisfactory, reaching 86% Accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S.: Automatic fruit and vegetable classification from images. Comput. Electron. Agric. 70(1), 96–104 (2010)

    Article  Google Scholar 

  2. Wajid, A., Singh, N.K.: Recognition of ripe, unripe and scaled condition of orange citrus based on decisión tree classification. In: International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (2018).

    Google Scholar 

  3. Aguirre Radilla, J., De La Cruz Gámez, E., Hernández Hernández, J. L., Carranza Gómez, J.: Clasificación del Mango Manila Aplicando. RVP-AI/ROC&C’2021 (2021)

    Google Scholar 

  4. Aguirre Radilla, J., De La Cruz Gámez, E., Hernández Hernández, J., Carranza Gómez, J., Montero Valverde, J., Martinez Arroyo, M.: Clasificación por Color y Textura del Mango Manila Aplicando. III Convención Científica Internacional (2021)

    Google Scholar 

  5. Albera, S.: Vehicle logo recognition using image. Atilim University, The Department of Software Engineering (2017)

    Google Scholar 

  6. Bhargava, A.: Fruits and vegetables quality evaluation using computer vision. J. King Saud Univ. 33(3), 243–257 (2018)

    Google Scholar 

  7. Bravo-Reyna, J.L., Montero-Valverde, J.A., Martínez-Arroyo, M., Hernández-Hernández, J.L.: Recognition of the damage caused by the cogollero worm to the corn plant, using artificial vision. In: Valencia-García, R., Alcaraz-Marmol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITI 2020. CCIS, vol. 1309, pp. 111–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62015-8_9

    Chapter  Google Scholar 

  8. Burger, W., Burge, M.J.: Digital Image Processing: An Algorithmic Introduction Using Java. Springer London, London (2016)

    Book  Google Scholar 

  9. Cavanillas, B.: (10 de Marzo de 2015). Láser para comprobar la madurez de la fruta sin estropearla. Obtenido de smartlighting: https://smart-lighting.es/laser-para-comprobar-la-madurez-de-la-fruta-sin-estropearla/

  10. Dadwal, M.: Color image segmentation for fruit ripeness detection. a review. Singapore (2012)

    Google Scholar 

  11. Dwairi, A.A.: Optimized True-Color Image Processing (2010). Recuperado de: https://www.researchgate.net/publication/260402622_Optimized_True-Color_Image_Processing

  12. Yossy, E.H., Pranata, J., Wijaya, T., Hermawan, H., Budiharto, W.: Mango fruit sortation system using neural network and computer vision. In: 2nd International Conference on Computer Science and Computational Intelligence, pp. 596–603 (2017)

    Google Scholar 

  13. Escobar, M.: Determinación del estado de madurez del aguacate mediante procesamiento de imágenes con la raspberry pi. Programa de Ingeniería Eléctrica (2018)

    Google Scholar 

  14. Forsyth, D.: Probability and Statistics for Computer Science. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-64410-3

    Book  MATH  Google Scholar 

  15. Geografía, I.N.: El mango en Guerrero: Censo Agropecuario 2007. Instituto Nacional de Estadística y Geografía (2007)

    Google Scholar 

  16. Choi, H.S., Cho, J.B.:A real-time Smart fruit quality grading system classi-fying by external appearence and internal flavor factors. In: IEEE International Conference on Industrial Technology (ICIT) (2018)

    Google Scholar 

  17. Hague, A.R.: Color segmentation in the HSI color space using the K-means algorithm. In: Proceedings of the SPIE 3026, Nonlinear Image Processing VIII (1997)

    Google Scholar 

  18. Hall-Beyer, M.: GLCM texture: a tutorial (2017)

    Google Scholar 

  19. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  20. Herrera, J.M.: Clasificación de los frutos de café según su estado de maduración y detección de la broca mediante técnicas de procesamiento de imágenes.Prospectiva, pp. 15–22 (2016)

    Google Scholar 

  21. Hu, M.D.: The potential of double K-means clustering for banana image segmentation. J. Food Process, 37(1), 10–18 (2013)

    Google Scholar 

  22. La Serna, N.C.: Procesamiento Digital de textura: Téc-nicas utilizadas en aplicaciones actuales de CBIR. Revista de investigación de sistemas e informática, 7(1), 57–64 (2010)

    Google Scholar 

  23. Leal., A. C.: Segmentación de imágenes por textura. Universidad de Concepción. Facultad de Ingeniería, Departamento de Ingeniería Eléctrica (2006)

    Google Scholar 

  24. Pham, V.H., Lee, B.R.: An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm. Vietnam J. Comput. Sci. 2(1), 25–33 (2014). https://doi.org/10.1007/s40595-014-0028-3

    Article  Google Scholar 

  25. Majed, O., Al-Dwairi, Z.A.: Optimized true-color image processing. World Appl. Sci. J. 8(10), 1175–1182 (2010)

    Google Scholar 

  26. Megha, P.A.: Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. In: 7th International Conference on Communication, Computing and Virtualization (2016)

    Google Scholar 

  27. Meruliya, T., Dhameliya, P., Patel, J., Panchal, D., Kadam, P., Naik, S.: Image processing for fruit shape and texture feature extraction - review. Int. J. Comput. Appl. 129(8), 30–33 (2015). https://doi.org/10.5120/ijca2015907000

    Article  Google Scholar 

  28. Otsu, N.: A treshold selection method from Gary level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Google Scholar 

  29. Patel, H.J.: Fruit detection using improved multiple features based algorithm. Int. J. Comput. Appl. 13(2), 1–5(2011).

    Google Scholar 

  30. Sahu, D., Potdar, R.M.: Defect identification and maturity detection of mango fruits using image analysis. Am. J. Artif. Intell. 1(1), 5–14 (2017). http://www.sciencepublishinggroup.com/j/ajai

  31. Rural, Planeación Agrícola Nacional 17–30.Secretaría de Cultura y Desarrollo Social (2017)

    Google Scholar 

  32. Rios-Diaz, J., Javier Martinez-Paya, J., del Bano Aledo, M.E.:Textural analysis by means of a grey level co-occurrence matrix method on patellar tendon ultrasonography is useful for the detection of histological changes after whole-body vibration training. Cultura, Ciencia y Deporte, 4(11), 91–102 (2009)

    Google Scholar 

  33. Mustafa, S., Dauda, A.B., Dauda, M.: Image processing and SVM classification for melanoma detection. In: International Conference on Computing Networking and Informatics (ICCNI), pp. 1–5 (2017)

    Google Scholar 

  34. Behera, S.K., Sangita, S., Rath, A.K., Sethy, P.K.: Automatic classification of mango using statistical feature and SVM. In: Biswas, U., Banerjee, A., Pal, S., Biswas, A., Sarkar, D., Haldar, S. (eds.) Advances in Computer, Communication and Control: Proceedings of ETES 2018, pp. 469–475. Springer Singapore, Singapore (2019). https://doi.org/10.1007/978-981-13-3122-0_47

    Chapter  Google Scholar 

  35. Subey, S.A.: Adapted Approach for Fruit Disease Identification us-ing Images. Int. J. Comput. Vis. Image Process. 44–58 (2012)

    Google Scholar 

  36. Zha, H., Chen, X., Wang, L., Miao, Q. (eds.): CCCV 2015. CCIS, vol. 546. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48558-3

    Book  Google Scholar 

  37. Long, N.T.M., Thinh, N.T.: Using machine learning to grade the mango’s quality based on external features captured by vision system. Appl. Sci. 10(17), 5775 (2020). https://doi.org/10.3390/app10175775

    Article  Google Scholar 

  38. Zhang, Y., Lian, J., Fan, M., Zheng, Y.: Deep indicator for fine-grained classification of banana’s ripening stages. EURASIP J. Image Video Process. 2018(1), 1–10 (2018). https://doi.org/10.1186/s13640-018-0284-8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Research and Graduate Studies, National Technology of Mexico, Acapulco Institute of Technology, Acapulco, Mexico

    Jacquelin Aguirre-Radilla, Eduardo De La Cruz-Gámez, Jorge Carranza-Gómez, José Antonio Montero-Valverde & Miriam Martínez-Arroyo

  2. Division of Research and Graduate Studies, National Technology of Mexico, Chilpancingo Institute of Technology, Chilpancingo, Mexico

    José Luis Hernández-Hernández

Authors
  1. Jacquelin Aguirre-Radilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eduardo De La Cruz-Gámez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Luis Hernández-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge Carranza-Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Antonio Montero-Valverde
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Miriam Martínez-Arroyo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Eduardo De La Cruz-Gámez or Miriam Martínez-Arroyo .

Editor information

Editors and Affiliations

  1. Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

  2. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Martha Bucaram-Leverone

  3. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Javier Del Cioppo-Morstadt

  4. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Néstor Vera-Lucio

  5. Universidad Agraria del Ecuador, Guayaquil, Ecuador

    Emma Jácome-Murillo

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aguirre-Radilla, J., De La Cruz-Gámez, E., Hernández-Hernández, J.L., Carranza-Gómez, J., Montero-Valverde, J.A., Martínez-Arroyo, M. (2022). Texture and Color-Based Analysis to Determine the Quality of the Manila Mango Using Digital Image Processing Techniques. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2022. Communications in Computer and Information Science, vol 1658. Springer, Cham. https://doi.org/10.1007/978-3-031-19961-5_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19961-5_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19960-8

  • Online ISBN: 978-3-031-19961-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation