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Precision Agriculture

, Volume 15, Issue 1, pp 80–94 | Cite as

Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform

  • S. Cubero
  • N. Aleixos
  • F. Albert
  • A. Torregrosa
  • C. Ortiz
  • O. García-Navarrete
  • J. Blasco
Article

Abstract

The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R2 coefficient of 0.993 for size estimation and an R2 coefficient of 0.918 for the colour index.

Keywords

Assisted harvesting Mobile platform Machine vision Smart camera Fruit pre-grading Citrus fruits 

Notes

Acknowledgments

This research work has been funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) and the European FEDER funds (projects RTA2009-00118-C02-01 and RTA2009-00118-C02-02). The authors wish to thank the collaboration of the company Argilés Diseny i Fabricació, S.L.

References

  1. Baeten, J., Donné, K., Boedrij, S., Beckers, W., & Claesen, E. (2008). Autonomous fruit picking machine: A robotic apple harvester. Springer Tracts in Advanced Robotics, 42, 531–539.CrossRefGoogle Scholar
  2. Blasco, J., Aleixos, N., Gómez-Sanchis, J., & Moltó, E. (2009a). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103, 137–145.CrossRefGoogle Scholar
  3. Blasco, J., Aleixos, N., Roger, J. M., Rabatel, G., & Moltó, E. (2002). Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149–157.CrossRefGoogle Scholar
  4. Blasco, J., Cubero, S., Gómez-Sanchis, J., Mira, P., & Moltó, E. (2009b). Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering, 90, 27–34.CrossRefGoogle Scholar
  5. Chong, V. K., Monta, M., Ninomiya, K., Kondo, N., Namba, K., Terasaki, E., et al. (2008). Development of mobile eggplant grading robot for dynamic in-field variability sensing––manufacture of robot and performance test. Engineering in Agriculture, Environment and Food, 1(2), 68–76.Google Scholar
  6. Coppock, G. E., & Jutras, P. J. (1960). Mechanizing citrus fruit harvesting. Transactions of the ASAE, 3(2), 130–132.CrossRefGoogle Scholar
  7. Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487–504.CrossRefGoogle Scholar
  8. Cubero, S., Moltó, E., Gutiérrez, A., Aleixos, N., García-Navarrete, O. L., Juste, F., et al. (2010). Real-time inspection of fruit on a mobile harvesting platform in field conditions using computer vision. Progress in Agricultural Engineering Science, 6, 1–16.CrossRefGoogle Scholar
  9. DOGV. (2006). Diari Oficial de la Comunitat Valenciana, 5346, 30321–30328.Google Scholar
  10. Edan, Y., Rogozin, D., Flash, T., & Miles, G. E. (2000). Robotic melon harvesting. IEEE Transactions on Robotics and Automation, 16(6), 831–834.CrossRefGoogle Scholar
  11. Ehsani, M. R., Grift, T. E., Maja, J. M., & Zhong, D. (2009). Two fruit counting techniques for citrus mechanical harvesting machinery. Computers and Electronics in Agriculture, 65(2), 186–191.CrossRefGoogle Scholar
  12. Feng, G., Qixin, C., & Masateru, N. (2008). Fruit detachment and classification method for strawberry harvesting robot. International Journal of Advanced Robotic Systems, 5(1), 41–48.Google Scholar
  13. Gómez-Sanchis, J., Gómez-Chova, L., Aleixos, N., Camps-Valls, G., Montesinos-Herrero, C., Moltó, E., et al. (2008). Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering, 89(1), 80–86.CrossRefGoogle Scholar
  14. HunterLab. (2008). Applications note, 8(9) http://www.hunterlab.com/appnotes/an08_96a.pdf. Accessed Nov 2012.
  15. Jiménez-Cuesta, M.J., Cuquerella, J., & Martínez-Jávega, J.M. (1981). Determination of a color index for citrus fruit degreening. In: Proceedings of the International Society of Citriculture, Tokyo (Japan), vol. 2 (pp. 750–753).Google Scholar
  16. Jutras, P.J., & Coppock, G.E. (1958). Mechanization of citrus fruit picking. Florida State Horticultural Society, 71, 201,204.Google Scholar
  17. Kohno, Y., Kondo, N., Iida, M., Kurita, M., Shiigi, T., Ogawa, Y., et al. (2011). Development of a mobile grading machine for citrus fruit. Engineering in Agriculture, Environment and Food, 4(1), 7–11.Google Scholar
  18. Kondo, N. (2009). Robotization in fruit grading system. Sensors and Instrumentation for Food Quality, 3, 81–87.CrossRefGoogle Scholar
  19. Lee, W. S., & Slaughter, D. C. (2004). Recognition of partially occluded plant leaves using a modified Watershed algorithm. Transactions of the ASAE, 47, 1269–1280.CrossRefGoogle Scholar
  20. Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1(1), 95–113.CrossRefGoogle Scholar
  21. Li, Z., Li, P., & Liu, J. (2011). Physical and mechanical properties of tomato fruits as related to robot harvesting. Journal of Food Engineering, 103(2), 170–178.CrossRefGoogle Scholar
  22. Lorente, D., Aleixos, N., Gómez-Sanchis, J., Cubero, S., García-Navarrete, O. L., & Blasco, J. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology, 5(4), 1121–1142.CrossRefGoogle Scholar
  23. Mazzetto, F., Calcante, A., Mena, A., & Vercesi, A. (2010). Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture. Precision Agriculture, 11(6), 636–649.CrossRefGoogle Scholar
  24. McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6(1), 7–23.CrossRefGoogle Scholar
  25. Mizushima, A., & Lu, R. (2011). Cost benefits analysis of in-field presorting for the apple industry. Applied Engineering in Agriculture, 27(1), 33–40.CrossRefGoogle Scholar
  26. Muscato, G., Prestifilippo, M., Abbate, N., & Rizzuto, I. (2005). A prototype of an orange picking robot: Past history and experimental results. Industrial Robot, 32(2), 128–138.CrossRefGoogle Scholar
  27. Nieuwenhuizen, A. T., Hofstee, J. W., & van Henten, E. J. (2010). Adaptive detection of volunteer potato plants in sugar beet fields. Precision Agriculture, 11, 433–447.CrossRefGoogle Scholar
  28. Official Journal of European Communities. (2001). 14.09.2001. pp. L244/12–L244/18. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2001:244:0012:0018:EN:PDF. Accessed May 2013.
  29. Ortiz, C., Blasco, J., Balasch, S., & Torregrosa, A. (2011). Shock absorbing surfaces for collecting fruit during the mechanical harvesting of citrus. Biosystems Engineering, 110, 2–9.CrossRefGoogle Scholar
  30. Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., & Morimoto, E. (2004). Mobile fruit grading robot (part1)––Development of a robotic system for grading sweet peppers. Journal of the Japanese Society of Agricultural Machinery (JSAM), 66(2), 113–122.Google Scholar
  31. Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., & Morimoto, E. (2005). Mapping yield and quality using the mobile fruit grading robot. Biosystems Engineering, 90(2), 135–142.CrossRefGoogle Scholar
  32. Ruiz-Altisent, M., Ortiz-Cañavate, J., & Valero, C. (2004). Fruit and vegetables harvesting systems. In: R. Dris and S. M. Jain (Eds.), Production practices and quality assessment of food crops, vol. 1: Preharvest practice (pp. 261–285). Dordrecht: Kluwer.Google Scholar
  33. Torregrosa, A., Gil, J., Ortiz, C., Ortí, E., & Martín, B. (2009). Mechanical harvesting of oranges and mandarins in Spain. Biosystems Engineering, 104(1), 18–24.CrossRefGoogle Scholar
  34. Vidal, A., Talens, P., Prats-Montalbán, J. M., Cubero, S., Albert, F., & Blasco, J. (2012). In-line estimation of the standard colour index of citrus fruits using a computer vision system developed for a mobile platform. Food and Bioprocess Technology,. doi: 10.1007/s11947-012-1015-2.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • S. Cubero
    • 1
  • N. Aleixos
    • 2
  • F. Albert
    • 2
  • A. Torregrosa
    • 3
  • C. Ortiz
    • 3
  • O. García-Navarrete
    • 1
    • 4
  • J. Blasco
    • 1
  1. 1.Centro de AgroingenieríaInstituto Valenciano de Investigaciones Agrarias (IVIA)Moncada (Valencia)Spain
  2. 2.Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser HumanoUniversitat Politècnica de ValènciaValenciaSpain
  3. 3.Dpto. de Ingeniería Rural y AgroalimentariaUniversitat Politècnica de ValènciaValenciaSpain
  4. 4.Dpto de Ingeniería Civil y AgrícolaUniversidad Nacional de Colombia, Sede BogotáBogotáColombia

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