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Incorporating Dilation Convolution into Mask Region Convolution Neural Network for Advanced Fruit Classification and Freshness Evaluation

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New Trends in Information and Communications Technology Applications (NTICT 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2096))

  • 13 Accesses

Abstract

Fruit supply and storage chains play a significant role in the food economies of all countries that export and consume these crops. Reducing the losses caused by the damage to these crops and organizing their marketing, relying on artificial intelligence methods, has begun to receive increasing attention from researchers. To ensure food safety and meet consumer expectations. This paper investigates the integration of dilation convolution into the Mask R-CNN framework to improve the accuracy of produce classification and detection of freshness. This paper seeks to address the challenges associated with traditional methods of fruit quality assessment, such as visual inspection and manual grading, which are time-consuming and subjective. To ensure the robustness of our approach, we collected a diverse set of complex images under various lighting conditions, and we implemented preprocessing techniques, such as histogram equalization and color correction, to enhance image quality. Using instance segmentation allowed us to distinguish and classify overlapped objects within each image. The practical results demonstrate that integrating dilation convolution improves the fruit classification, with an accuracy of 97.8% over the baseline Mask R-CNN. Real-time images and video were applied using the trained model, proving its practical utility in dynamic environments. It demonstrates the potential for direct object detection and classification by proposing a valuable tool to enhance productivity and ensure product quality.

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References

  1. Kanupuru, P., Uma Reddy, N.V.: A deep learning approach to detect spoiled fruits. WSEAS Trans. Comput. Res. 10(July), 74–87 (2022). https://doi.org/10.37394/232018.2022.10.10

  2. Faisal, M., Albogamy, F., Elgibreen, H., Algabri, M., Alqershi, F.A.: Deep learning and computer vision for estimating date fruits type, maturity level, and weight. IEEE Access 8, 206770–206782 (2020). https://doi.org/10.1109/ACCESS.2020.3037948

    Article  Google Scholar 

  3. Palakodati, S.S.S., Chirra, V.R.R., Yakobu, D., Bulla, S.: Fresh and Rotten Fruits Classification Using CNN and Transfer Learning. Rev. d’Intelligence Artif. 34(5), 617–622 (2020). https://doi.org/10.18280/ria.340512

  4. Ireri, D., Belal, E., Okinda, C., Makange, N., Ji, C.: A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture 2, 28–37 (2019). https://doi.org/10.1016/j.aiia.2019.06.001

    Article  Google Scholar 

  5. O’Mahony, N., et al.: Deep learning vs. traditional computer vision. In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1 1, pp. 128–144. Springer International Publishing (2020). https://doi.org/10.1007/978-3-030-17795-9_10

  6. Lee, H.S., Shin, B.S.: Potato detection and segmentation based on mask R-CNN. J. Biosys. Eng. 45, 233–238 (2020). https://doi.org/10.1007/s42853-020-00063-w

    Article  Google Scholar 

  7. Roy, K., Chaudhuri, S.S., Pramanik, S.: Deep learning based real-time Industrial framework for rotten and fresh fruit detection using semantic segmentation. Microsyst. Technol. 27, 3365–3375 (2021). https://doi.org/10.1007/s00542-020-05123-x

    Article  Google Scholar 

  8. Tian, Y., Li, E., Liang, Z., Tan, M., He, X.: Diagnosis of typical apple diseases: a deep learning method based on multi-scale dense classification network. Front. Plant Sci. 12, 698474 (2021). https://doi.org/10.3389/fpls.2021.698474

    Article  Google Scholar 

  9. Dhiman, P., et al.: A novel deep learning model for detection of severity level of the disease in citrus fruits. Electronics 11(3), 495 (2022). https://doi.org/10.3390/electronics11030495

    Article  Google Scholar 

  10. Minagawa, D., Kim, J.: Prediction of harvest time of tomato using mask R-CNN. Agri. Engineering 4(2), 356–366 (2022). https://doi.org/10.3390/agriengineering4020024

    Article  Google Scholar 

  11. Lu, J., et al.: Citrus green fruit detection via improved feature network extraction. Frontiers in Plant Science 13, 946154 (2022). https://doi.org/10.3389/fpls.2022.946154

  12. Cong, P., Li, S., Zhou, J., Lv, K., Feng, H.: Research on instance segmentation algorithm of greenhouse sweet pepper detection based on improved mask RCNN. Agronomy 13(1), 196 (2023). https://doi.org/10.3390/agronomy13010196

    Article  Google Scholar 

  13. Singh, G., Mittal, A.: Various image enhancement techniques-a critical review. Int. J. Inno. Sci. Res. 10(2), 267–274 (2014)

    Google Scholar 

  14. Qi, Y., et al.: A comprehensive overview of image enhancement techniques. Archives of Computational Methods in Engineering, 1–25 (2021). https://doi.org/10.1007/s11831-021-09587-6

  15. Maini, R., Aggarwal, H.: A comprehensive review of image enhancement techniques. ar**v Prepr. ar**v1003.4053 (2010). https://doi.org/10.48550/ar**v.1003.4053

  16. Tian, D., Han, Y., Wang, B., Guan, T., Gu, H., Wei, W.: Review of object instance segmentation based on deep learning. J. Electron. Imaging 31(4), 041205 (2022). https://doi.org/10.1117/1.jei.31.4.041205

    Article  Google Scholar 

  17. Lin, G., Wu, Q., Qiu, L., Huang, X.: Image super-resolution using a dilated convolutional neural network. Neurocomputing 275, 1219–1230 (2018). https://doi.org/10.1016/j.neucom.2017.09.062

    Article  Google Scholar 

  18. Zhang, Z., Wang, X., Jung, C.: DCSR: Dilated convolutions for single image super-resolution. IEEE Trans. Image Process. 28(4), 1625–1635 (2018). https://doi.org/10.1109/TIP.2018.2877483

    Article  MathSciNet  Google Scholar 

  19. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 2961–2969 (2017). https://doi.org/10.1109/TPAMI.2018.2844175

  20. Hafiz, A.M., Bhat, G.M.: A survey on instance segmentation: state of the art. Int. J. Multimed Info. Retr. 9, 171–189 (2020). https://doi.org/10.1007/s13735-020-00195-x

    Article  Google Scholar 

  21. Amo-Boateng, M., Sey, N.E.N., Amproche, A.A., Domfeh, M.K.: Instance segmentation scheme for roofs in rural areas based on Mask R-CNN. The Egypt. J. Remote Sens. Space Sci. 25(2), 569–577 (2022). https://doi.org/10.1016/j.ejrs.2022.03.017

    Article  Google Scholar 

  22. Nie, X., Duan, M., Ding, H., Hu, B., Wong, E.K.: Attention mask R-CNN for ship detection and segmentation from remote sensing images. Ieee Access 8, 9325–9334 (2020). https://doi.org/10.1109/ACCESS.2020.2964540

    Article  Google Scholar 

  23. Kumar, S.D., Esakkirajan, S., Bama, S., Keerthiveena, B.: A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocess. Microsyst. 76, 103090 (2020). https://doi.org/10.1016/j.micpro.2020.103090

    Article  Google Scholar 

  24. Zu, L., Zhao, Y., Liu, J., Su, F., Zhang, Y., Liu, P.: Detection and segmentation of mature green tomatoes based on mask R-CNN with automatic image acquisition approach. Sensors 21(23), 7842 (2021). https://doi.org/10.3390/s21237842

    Article  Google Scholar 

  25. Mimma, N.E.A., Ahmed, S., Rahman, T., Khan, R.: Fruits classification and detection application using deep learning. Sci. Program. 2022 (2022). https://doi.org/10.1155/2022/4194874

  26. Mai, X., Zhang, H., Meng, M.Q.H.: Faster R-CNN with classifier fusion for small fruit detection. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 7166–7172. IEEE (2018). https://doi.org/10.1109/ICRA.2018.8461130

  27. Nagesh Appe, S.R., Arulselvi, G., Balaji, G.: Tomato Ripeness Detection and Classification using VGG based CNN Models. Int. J. Intell. Sys. Appli. Eng. 11(1), 296–302 (2023). Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2538

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Acknowledgments

The author is grateful to Mustansiriyah University (www.uomustansiriyah.edu.iq) in Baghdad –Iraq for its assistance and encouragement with the current study.

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Authors and Affiliations

  1. Electrical Engineering Department, Collage of Engineering, Al Mustansiriyah University, Baghdad, Iraq

    Rafah Adnan Shandookh, Tariq M. Salman & Abbas H. Miry

Authors
  1. Rafah Adnan Shandookh
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  2. Tariq M. Salman
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  3. Abbas H. Miry
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Corresponding author

Correspondence to Rafah Adnan Shandookh .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  3. University of Babylon, Babylon, Iraq

    Safaa O. Al-Mamory

  4. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  5. University of Information Technology and Communications, Baghdad, Iraq

    Ali N. Al-Shuwaili

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. University of Information Technology and Communications, Baghdad, Iraq

    Rula A. Hamid

  8. University of Babylon, Babylon, Iraq

    Ali K. Idrees

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Shandookh, R.A., Salman, T.M., Miry, A.H. (2024). Incorporating Dilation Convolution into Mask Region Convolution Neural Network for Advanced Fruit Classification and Freshness Evaluation. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2023. Communications in Computer and Information Science, vol 2096. Springer, Cham. https://doi.org/10.1007/978-3-031-62814-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-62814-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-62813-9

  • Online ISBN: 978-3-031-62814-6

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