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An Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills

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Optimization, Learning Algorithms and Applications (OL2A 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1982 ))

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Abstract

Identifying the maturation stage is an added value for olive oil producers and consumers, whether this is done to predict the best harvest time, give us more information about the olive oil, or even adapt techniques and extraction parameters in the olive oil mill. In this way, the proposed work presents a new method to identify and count the number of olives that enter the mill as well as their stage of maturation. It is based on artificial intelligence (AI) and deep learning algorithms, using the two most recent versions of YOLO, YOLOv7 and YOLOv8. The obtained results demonstrate the possibility of using this type of application in a real environment, managing to obtain a mAP of approximately 79% with YOLOv8 in the five maturation stages, with a processing rate of approximately 16 FPS increasing this with YOLOv7 to 36.5 FPS reaching a 66% mAP.

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Notes

  1. 1.

    https://github.com/WongKinYiu/yolov7 and https://github.com/ultralytics/ultralytics Accessed on June 15, 2023.

References

  1. Agati, G., Pinelli, P., Ebner, S., Romani, A., Cartelat, A., Cerovic, Z.: Nondestructive evaluation of anthocyanins in olive (olea europaea) fruits by in situ chlorophyll fluorescence spectroscopy. J. Agricult. Food Chem. 53, 1354–1363 (2005). https://doi.org/10.1021/jf048381d

  2. Aguilera, M., et al.: Characterisation of virgin olive oil of italian olive cultivars: ‘frantoio’ and ‘leccino’, grown in andalusia. Food Chem. 89, 387–391 (2005). https://doi.org/10.1016/j.foodchem.2004.02.046

  3. Aguilera Puerto, D., Cáceres Moreno, Ó., Martínez Gila, D.M., Gómez Ortega, J., Gámez García, J.: Online system for the identification and classification of olive fruits for the olive oil production process. J. Food Measur. Character. 13(1), 716–727 (2019). https://doi.org/10.1007/s11694-018-9984-0

  4. Aparicio, R., Ferreiro, L., Alonso, V.: Effect of clima on the chemical, composition of virgin olive oil. Anal. Chim. Acta 292(3), 235–241 (1994). https://doi.org/10.1016/0003-2670(94)00065-4

  5. Avila, F., Mora, M., Oyarce, M., Zuñiga, A., Fredes, C.: A method to construct fruit maturity color scales based on support machines for regression: application to olives and grape seeds. J. Food Eng. 162, 9–17 (2015). https://doi.org/10.1016/j.jfoodeng.2015.03.035

  6. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection (2020)

    Google Scholar 

  7. Boyd, K., Costa, V.S., Davis, J., Page, D.: Unachievable region in precision-recall space and its effect on empirical evaluation (2012)

    Google Scholar 

  8. Bradski, G.: The OpenCV Library. Dr. Dobb’s J. Softw. Tools (2000)

    Google Scholar 

  9. Council, I.O.: Guide for the Determination of the Characteristics of Oil-Olives. Technical Document COI/OH/Doc. No. 1, IOC, Madrid (2011)

    Google Scholar 

  10. Cárdenas-Pérez, S., et al.: Evaluation of the ripening stages of apple (golden delicious) by means of computer vision system. Biosyst. Eng. 159, 46–58 (2017). https://doi.org/10.1016/j.biosystemseng.2017.04.009

  11. Diaz, R., Gil, L., Serrano, C., Blasco, M., Molto, E., Blasco, J.: Comparison of three algorithms in the classification of table olives by means of computer vision. J. Food Eng. 61(1), 101–107 (2004). https://doi.org/10.1016/S0260-8774(03)00191-2

  12. Garcia, J.M., Seller, S., Perez-Camino, M.C.: Influence of fruit ripening on olive oil quality. J. Agric. Food Chem. 44(11), 3516–3520 (1996)

    Article  Google Scholar 

  13. García, J., Yousfi, K.: Non-destructive and objective methods for the evaluation of the ripening level of olive fruit. Eur. Food Res. Technol. 221, 538–541 (2005). https://doi.org/10.1007/s00217-005-1180-x

  14. Giuffre, A.M.: Influence of harvest year and cultivar on wax composition of olive oils. Eur. J. Lipid Sci. Technol. 115(5), 549–555 (2013). https://doi.org/10.1002/ejlt.201200235

  15. Gorini, I., Iorio, S., Ciliberti, R., Licata, M., Armocida, G.: Olive oil in pharmacological and cosmetic traditions. J. Cosmet. Dermatol. 18(5), 1575–1579 (2019). https://doi.org/10.1111/jocd.12838

  16. Gracia, A., León, L.: Non-destructive assessment of olive fruit ripening by portable near infrared spectroscopy. Grasas Aceites 62(3), 268–274 (2011). https://doi.org/10.3989/gya.089610

  17. Guzmán, E., Baeten, V., Pierna, J., García-Mesa, J.A.: Determination of the olive maturity index of intact fruits using image analysis. J. Food Sci. Technol. 52, 1462–1470 (2015)

    Article  Google Scholar 

  18. Khosravi, H., Saedi, S., Rezaei, M.: Real-time recognition of on-branch olive ripening stages by a deep convolutional neural network. Scientia Horticult. 287, 110252 (2021). https://doi.org/10.1016/j.scienta.2021.110252

  19. Li, C., et al.: Yolov6: a single-stage object detection framework for industrial applications (2022)

    Google Scholar 

  20. Lupi, F.R., Gentile, L., Gabriele, D., Mazzulla, S., Baldino, N., de Cindio, B.: Olive oil and hyperthermal water Bigels for cosmetic uses. J. Colloid Interface Sci. 459, 70–78 (2015). https://doi.org/10.1016/j.jcis.2015.08.013

  21. Matos, L., et al.: Chemometric characterization of three varietal olive oils (cvs. cobrançosa, madural and verdeal transmontana) extracted from olives with different maturation indices. Food Chem. 102, 406–414 (2007). https://doi.org/10.1016/j.foodchem.2005.12.031

  22. Meksi, N., Haddar, W., Hammami, S., Mhenni, M.F.: Olive mill wastewater: a potential source of natural dyes for textile dyeing. Indust. Crops Prod. 40, 103–109 (2012). https://doi.org/10.1016/j.indcrop.2012.03.011

  23. Mendoza, F., Aguilera, J.: Application of image analysis for classification of ripening bananas. J. Food Sci. 69, E471–E477 (2006). https://doi.org/10.1111/j.1365-2621.2004.tb09932.x

  24. Monteleone, E., Caporale, G., Carlucci, A., Pagliarini, E.: Optimisation of extra virgin olive oil quality. J. Sci. Food Agric. 77(1), 31–37 (1998). https://doi.org/10.1002/(SICI)1097-0010(199805)77:1<31::AID-JSFA998>3.0.CO;2-F

    Article  Google Scholar 

  25. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2016)

    Google Scholar 

  26. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger (2016)

    Google Scholar 

  27. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018)

    Google Scholar 

  28. Riquelme, M.T., Barreiro, P., Ruiz-Altisent, M., Valero, C.: Olive classification according to external damage using image analysis. J. Food Eng. 87(3), 371–379 (2008). https://doi.org/10.1016/j.jfoodeng.2007.12.018

  29. Salvucci, G., et al.: Fast olive quality assessment through RGB images and advanced convolutional neural network modeling. Eur. Food Res. Technol. 248, 1395–1405 (2022). https://doi.org/10.1007/s00217-022-03971-7

  30. Tan, L., Huangfu, T., Wu, L., Chen, W.: Comparison of yolo v3, faster r-cnn, and ssd for real-time pill identification (2021)

    Google Scholar 

  31. Tzutalin: Labelimg. Free Software: MIT License (2015). https://github.com/tzutalin/labelImg

  32. Uceda, M., Frias, L.: Harvest dates, evolution of the fruit oil content, oil composition and oil quality. In: Proceedings II, Seminario Oleícola Internacional, COI, Córdoba, pp. 125–128 (1975)

    Google Scholar 

  33. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022)

    Google Scholar 

  34. Wu, D., Sun, D.W.: Colour measurements by computer vision for food quality control - a review. Trends Food Sci. Technol. 29(1), 5–20 (2013). https://doi.org/10.1016/j.tifs.2012.08.004

  35. Yorulmaz, A., Erinç, H., Tekin, A.: Changes in olive and olive oil characteristics during maturation. J. Am. Oil Chem. Soc. 90, 647–658 (2013). https://doi.org/10.1007/s11746-013-2210-7

  36. Zhang, B., et al.: Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: a review. Food Res. Int. 62, 326–343 (2014). https://doi.org/10.1016/j.foodres.2014.03.012

  37. Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios (2021)

    Google Scholar 

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Acknowledgements

This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF). The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), ALGORITMI (UIDB/00319/2020) and SusTEC (LA /P/0007/2021).

Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    João Mendes, José Lima, Paulo Leitão & Ana I. Pereira

  2. ALGORITMI Center, University of Minho, 4710-057, Braga, Portugal

    João Mendes, Lino A. Costa & Ana I. Pereira

  3. Centro de Investigação de Montanha (CIMO), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    Nuno Rodrigues

  4. Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    João Mendes, José Lima, Nuno Rodrigues, Paulo Leitão & Ana I. Pereira

Authors
  1. João Mendes
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  2. José Lima
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  3. Lino A. Costa
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  4. Nuno Rodrigues
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  5. Paulo Leitão
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  6. Ana I. Pereira
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Corresponding author

Correspondence to João Mendes .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Azores, Ponta Delgada, Portugal

    Armando Mendes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

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Mendes, J., Lima, J., Costa, L.A., Rodrigues, N., Leitão, P., Pereira, A.I. (2024). An Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills. In: Pereira, A.I., Mendes, A., Fernandes, F.P., Pacheco, M.F., Coelho, J.P., Lima, J. (eds) Optimization, Learning Algorithms and Applications. OL2A 2023. Communications in Computer and Information Science, vol 1982 . Springer, Cham. https://doi.org/10.1007/978-3-031-53036-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-53036-4_5

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