Skip to main content

Rotten Fruit Detection Using a One Stage Object Detector

  • Conference paper
  • First Online:
Advances in Computational Intelligence (MICAI 2020)

Abstract

Digital images and computer sciences have become two powerful tools in several areas, such as astronomy, medicine, forensics, etc. In the last years, computer sciences are getting involved in agricultural and food science to decide based on estimated or actual parameters named features. Rottenness is the state of decomposing or decaying the quality of the fruit, which not only affects the taste and appearance but also modifies its nutritional composition, causing the presence of mycotoxins dangerous for humans. Nowadays, rottenness detection is carried out using human inspection or using Ultraviolet light to highlight spots of rottenness represented as fluorescence. Recent computer vision approaches address this problem using hyperspectral imaging systems. In this paper, we propose to use a one-stage object detector inspired by RetinaNet to detect whether a fruit is fresh or rotten. One of the main stages of RetinaNet is based on computing a multi-scale convolutional feature pyramid network on top of a backbone. Therefore, in this work, we analyze the performance of RetinaNet using different artificial neural networks as backbone to determine the highest accuracy for fruit and rottenness detection. The experiments were done using a dataset composed of 13599 images divided by 6 classes, 3 fresh fruits, and 3 rotten fruits. The performance evaluation considers the mean average precision in the detection and the inference time of tested backbone models.

Supported by organization Universidad Panamericana.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arzate-Vázquez, I., et al.: Image processing applied to classification of avocado variety hass (persea americana mill) during the ripening process. Food Bioprocess Technol. 4(7), 1307–1313 (2011)

    Article  Google Scholar 

  2. Bhargava, A., Bansal, A.: Fruits and vegetables quality evaluation using computer vision: a review. Journal of King Saud University - Computer and Information Sciences, pp. 1–15 (2018)

    Google Scholar 

  3. Calvo, H., Moreno-Armendáriz, M.A., Godoy-Calderón, S.: A practical framework for automatic food products classification using computer vision and inductive characterization. Neurocomputing 175, 911–923 (2016)

    Article  Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. da Costa, A.Z., Figueroa, H.E.H., Fracarolli, J.A.: Computer vision based detection of external defects on tomatoes using deep learning. Biosyst. Eng. 190, 131–144 (2020)

    Article  Google Scholar 

  6. Fan, S., et al.: On line detection of defective apples using computer vision system combined with deep learning methods. J. Food Eng. 286, 110102 (2020)

    Article  Google Scholar 

  7. Goel, L., Raman, S., Dora, S.S., Bhutani, A., Aditya, A.S., Mehta, A.: Hybrid computational intelligence algorithms and their applications to detect food quality. Artif. Intell. Rev. 53(2), 1415–1440 (2019). https://doi.org/10.1007/s10462-019-09705-8

    Article  Google Scholar 

  8. Gómez-Sanchis, J., Martín-Guerrero, J.D., Soria-Olivas, E., Martínez-Sober, M., Magdalena-Benedito, R., Blasco, J.: Detecting rottenness caused by penicillium genus fungi in citrus fruits using machine learning techniques. Expert Syst. Appl. 39(1), 780–785 (2012)

    Article  Google Scholar 

  9. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)

    Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  11. Hoang, T.M., Nguyen, P.H., Truong, N.Q., Lee, Y.W., Park, K.R.: Deep retinanet-based detection and classification of road markings by visible light camera sensors. Sensors (Basel, Switz.) 19, 281 (2019)

    Article  Google Scholar 

  12. ITU: H.264 : Advanced video coding for generic audiovisual services (2018). urlhttps://www.itu.int/rec/T-REC-H.264-201906-I/en

    Google Scholar 

  13. Jiao, L., et al.: A survey of deep learning-based object detection. IEEE Access 7, 128837–128868 (2019)

    Article  Google Scholar 

  14. Kalluri, S.R.: Fruits: fresh and rotten for classification Dataset (2018). urlhttps://www.kaggle.com/sriramr/fruits-fresh-and-rotten-for-classification

    Google Scholar 

  15. Lin, T., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)

    Article  Google Scholar 

  16. Lin, T.-Y., et al.: Microsoft COCO: Common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  17. Liu, W., et al.: SSD: Single shot multibox detector. In: ECCV (2016)

    Google Scholar 

  18. Nosseir, A., Ahmed, S.E.A.: Automatic classification for fruits’ types and identification of rotten ones using k-nn and svm. Int. J. Online Biomed. Eng. 15(03), 47–61 (2019)

    Article  Google Scholar 

  19. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  20. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  21. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  22. Zhang, Y., Wu, L.: Classification of fruits using computer vision and a multiclass support vector machine. Sensors (Basel, Switz.) 12, 12489–12505 (2012)

    Article  Google Scholar 

  23. Zhu, X., Li, G.: Rapid detection and visualization of slight bruise on apples using hyperspectral imaging. Int. J. Food Prop. 22(1), 1709–1719 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Perez-Daniel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Perez-Daniel, K., Fierro-Radilla, A., Peñaloza-Cobos, J.P. (2020). Rotten Fruit Detection Using a One Stage Object Detector. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60887-3_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60886-6

  • Online ISBN: 978-3-030-60887-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics