DOI QR코드

DOI QR Code

A study of Strawberry Maturity Classification Using Improved Faster R-CNN

  • Taewook Kim (Department of Smart System, Kwangwoon University) ;
  • Heejun Youn (Department of Plasma Bio Display, Kwangwoon University) ;
  • Seunghyun Lee (Ingenium College Liberal Arts, Kwangwoon University) ;
  • Soonchul Kwon (Graduate School of Smart Convergence, Kwangwoon University)
  • Received : 2024.09.16
  • Accepted : 2024.09.26
  • Published : 2024.11.30

Abstract

In strawberry cultivation, maturity classification plays an important role in ensuring the efficiency and quality of harvesting. In this study, we propose an Improved Faster R-CNN model to address these challenges, using MobileNetV3-Large as the backbone network to achieve a lightweight model, and introducing RoI Align to improve the spatial accuracy of the feature map. Experiments are conducted using the KGCV_Strawberry dataset, with precision, recall, F1 score, and mean average precision (mAP) measured for performance evaluation. The experimental results show that the proposed model achieves an average precision of 71.35%, recall of 71.07%, and F1 score of 71.21% across all classes. In particular, the proposed model achieves 63% performance on mAP0.5 and 58% performance on mAP0.5:0.95, which is comparable to existing ResNet-based models while achieving faster inference speed. The proposed model achieves a processing speed of 27.6543 ms, which is about 2 ms faster than existing ResNet-based models. This indicates that the goal of creating a lightweight model with improved image processing capability was achieved with minimal performance degradation. This research is expected to contribute to the development of automated strawberry cultivation systems in greenhouse environments and has the potential to be applied to various agricultural environments in the future.

Keywords

Acknowledgement

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2024-RS-2023-00258639) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).

References

  1. F. Giampieri, S Tulipani, M. Alvarez-Suarez, L. Quiles, B. Mezzetti and M. Battino., "The strawberry: composition, nutritional quality, and impact on human health," Nutrition, Vol. 28, No. 1, pp. 9-19, 2012. DOI: https://doi.org/10.1016/j.nut.2011.08.009
  2. N. Wang, G. Li, J. Yang, Y. Zhang, and H. Wang, "Effects of Climate Change on Strawberry Production and Quality in East Asia," in Horticultural Science & Technology, Springer, vol. 33, no. 4, pp. 515-525, 2015. DOI: https://doi.org/10.1007/s10341-015-0249-2.
  3. Y. Chen, J. Bin, and C. Kang, "Application of machine vision and convolutional neural networks in discriminating tobacco leaf maturity on mobile devices," Smart Agricultural Technology, vol. 5, p. 100322, 2023. DOI: https://doi.org/10.1016/j.atech.2023.100322.
  4. Q. Feng, G. Zhou, Y. Wang, L. Yang, and H. Wang, "Strawberry harvesting robot for elevated-trough culture," International Journal of Agricultural and Biological Engineering, Vol. 12, No. 1, pp. 27-37, 2019. DOI: https://doi.org/10.25165/j.ijabe.20191201.4634
  5. A. Kamilaris and F. X. Prenafeta-Boldu, "Deep learning in agriculture: A survey," Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018. DOI: https://doi.org/10.1016/j.compag.2018.02.016.
  6. Y. Xiong, Y. Ge, L. Grimstad, and P. J. From, "An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation," Journal of Field Robotics, Vol. 37, No. 2, pp. 202-224, 2020. DOI: https://doi.org/10.1002/rob.21889
  7. R. Girshick, "Fast R-CNN", Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, 2015. DOI: https://doi.org/10.1109/ICCV.2015.169
  8. Y. Li, J. Zhang, H. Zhang, C. Liu, and Z. Wang, "YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5," Agronomy, Vol. 13, No. 7, 1901, 2023. DOI: https://doi.org/10.3390/agronomy13071901
  9. C. Zhou, J. Lin, S. Liu, Y. Liu, and Y. Wang, "A Novel Greenhouse-Based System for the Detection and Plumpness Assessment of Strawberry Using an Improved Deep Learning Technique," Frontiers in Plant Science, Vol. 11, 559, 2020. DOI: https://doi.org/10.3389/fpls.2020.00559
  10. Y. Wang, Z. Zhang, H. Li, L. Li, and Y. Liu, "DSE-YOLO: Detail Semantics Enhancement YOLO for Multi-Stage Strawberry Detection," Computers and Electronics in Agriculture, Vol. 200, 107057, 2022. DOI: https://doi.org/10.1016/j.compag.2022.107057
  11. X. Q. Yue, Y. X. Liu, L. H. Zhang, and Z. Y. Zhang, "A smart data-driven rapid method to recognize the strawberry maturity," Information Processing in Agriculture, Vol. 7, No. 4, pp. 575-584, 2020. DOI: https://doi.org/10.1016/j.inpa.2019.10.005
  12. X. Zhou, Z. Zeng, Y. Wang, J. Cao, and L. Zhang, "Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning," Smart Agricultural Technology, Vol. 1, 100001, 2021. DOI: https://doi.org/10.1016/j.atech.2021.100001
  13. Z. Tao, Y. Wu, Q. Huang, X. Li, and Y. Zhang, "Strawberry Maturity Recognition Based on Improved YOLOv5," Agronomy, Vol. 14, No. 3, 460, 2024. DOI: https://doi.org/10.3390/agronomy14030460
  14. Q. Yang, L. Zhang, Y. Wang, Y. Liu, and J. Chen, "Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision," Computers and Electronics in Agriculture, Vol. 220, 108911, 2024. DOI: https://doi.org/10.1016/j.compag.2024.108911
  15. R. Avenash and P. Viswanath, "Semantic Segmentation of Satellite Images using a Modified CNN with Hard-Swish Activation Function," Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), pp. 413-420, 2019. DOI: https://doi.org/10.5220/0007469604130420
  16. A. Howard, M. Sandler, G. Chu, L. C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam, "Searching for MobileNetV3," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1314-1324, 2019. DOI: https://doi.org/10.1109/ICCV.2019.00140
  17. K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961-2969, 2017. DOI: https://doi.org/10.1109/ICCV.2017.322