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Vision System for Pit Detection in Cherries

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Image Processing and Communications (IP&C 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1062))

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Abstract

In the following paper, the results of studies on the impact of lighting configuration on the quality of seed detection in cherries have been presented. The general concept of a vision system for cherry pits detection has been proposed. The two types of light were considered and characterized. The results of cherry classification confirm the effectiveness of the proposed solution and allow to indicate a better solution.

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References

  1. Qin, J., Lu, R.: Detection of pits in tart cherries by hyperspectral transmission imaging. Trans. ASAE 48, 1963–1970 (2005)

    Article  Google Scholar 

  2. Siedliska, A., Zubik, M., Baranowski, P., Mazurek, W.: Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data. Int. Agrophys. 31, 539–549 (2017)

    Article  Google Scholar 

  3. Donis-Gonzǎlez, I.R., Guyer, D.E., Kavdir, I., Shahriari, D., Pease, A.: Development and applicability of an agarose-based tart cherry phantom for computer tomography imaging. J. Food Meas. Charact. 9(3), 290–298 (2015)

    Article  Google Scholar 

  4. Kawano, S.: Past, present and future near infrared spectroscopy applications for fruit and vegetables. NIR News 27(1), 7–9 (2016)

    Article  Google Scholar 

  5. Lu, Y., Huang, Y., Lu, R.: Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review. Appl. Sci. 7(2), 189 (2017)

    Article  MathSciNet  Google Scholar 

  6. Muresan, H., Oltean, M.: Fruit recognition from images using deep learning (2018)

    Google Scholar 

  7. Yeong, T.J., Jern, K.P., Yao, L.K., Hannan, M.A., Hoon, H.T.G.: Applications of photonics in agriculture sector: a review. Open Access Mol. 24(10), 24 (2019)

    Google Scholar 

  8. Meruliya, T., Dhameliya, P., Jainish, P., Dilav, P., Pooja, K., Sapan, N.: Image processing for fruit shape and texture feature extraction - review. Int. J. Comput. Appl. 129, 30–33 (2015)

    Google Scholar 

  9. Bhargava, A., Bansal, A.: Fruits and vegetables quality evaluation using computer vision: a review. J. King Saud Univ. - Comput. Inf. Sci. (2018)

    Google Scholar 

  10. Gongal, A.A., Karkee, S., Manoj Zhang, Q., Lewis, K.: Sensors and systems for fruit detection and localization: a review. Cmput. Electron. Agric. 116, 8–19 (2015)

    Article  Google Scholar 

  11. Hameed, K., Chai, D., Rassau, A.: A comprehensive review of fruit and vegetable classification techniques. Image Vis. Comput. 80, 24–44 (2018)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the National Centre for Research and Development, project POIR.01.01.01-00-1045/17.

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Authors and Affiliations

  1. Institute of Microelectronics and Optoelectronics, Warsaw University of Technology, Nowowiejska 15/19, St., Warsaw, Poland

    Piotr Garbat, Piotr Sadura & Agata Olszewska

  2. Uniwentech, Nowy Rynek 1, Kiernozia, Poland

    Piotr Maciejewski

Authors
  1. Piotr Garbat
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  2. Piotr Sadura
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  3. Agata Olszewska
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  4. Piotr Maciejewski
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Corresponding author

Correspondence to Piotr Garbat .

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Editors and Affiliations

  1. Institute of Telecommunications and Computer Science, University of Science and Technology (UTP), Bydgoszcz, Poland

    Michał Choraś

  2. Department of Telecommunications, Computer Sciences and Electrical Engineering, University of Science and Technology (UTP), Bydgoszcz, Poland

    Ryszard S. Choraś

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Garbat, P., Sadura, P., Olszewska, A., Maciejewski, P. (2020). Vision System for Pit Detection in Cherries. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications. IP&C 2019. Advances in Intelligent Systems and Computing, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-31254-1_20

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