Open Access
Issue
E3S Web Conf.
Volume 658, 2025
Third International Conference of Applied Industrial Engineering: Intelligent Models and Data Engineering (CIIA 2025)
Article Number 03003
Number of page(s) 12
Section Intelligent Connectivity
DOI https://doi.org/10.1051/e3sconf/202565803003
Published online 13 November 2025
  1. E. Elbasi, N. Mostafa, Z. AlArnaout, A.I. Zreikat, E. Cina, G. Varghese, A. Shde-fat, A.E. Topcu, W. Abdelbaki, S. Mathew et al., Artificial intelligence technology in the agricultural sector: A systematic literature review, IEEE access 11, 171 (2022). 10.1109/ACCESS.2022.3232485 [Google Scholar]
  2. S. Ghazal, A. Munir, W.S. Qureshi, Computer vision in smart agriculture and preci-sion farming: Techniques and applications, Artificial Intelligence in Agriculture 13, 64 (2024). https://doi.org/10.1016/j.aiia.2024.06.004 [Google Scholar]
  3. V. Moya, A. Quito, A. Pilco, J.P. Vásconez, C. Vargas, Crop detection and maturity classification using a yolov5-based image analysis, Emerging Science Journal 8, 496 (2024). [Google Scholar]
  4. J. Vásconez, I. Vásconez, V. Moya, M. Calderón-Díaz, M. Valenzuela, X. Be-soain, M. Seeger, F. Auat Cheein, Deep learning-based classification of vi-sual symptoms of bacterial wilt disease caused by ralstonia solanacearum in tomato plants, Computers and Electronics in Agriculture 227, 109617 (2024). https://doi.org/10.1016/j.compag.2024.109617 [Google Scholar]
  5. X. Zhang, J. Bu, X. Zhou, X. Wang, Automatic pest identification system in the greenhouse based on deep learning and machine vision, Frontiers in Plant Science 14, 1255719 (2023). 10.3389/fpls.2023.1255719 [Google Scholar]
  6. H. Tian, T. Wang, Y. Liu, X. Qiao, Y. Li, Computer vision technology in agri-cultural automation—a review, Information processing in agriculture 7, 1 (2020). 10.1016/j.inpa.2019.09.006 [Google Scholar]
  7. J. Chen, J. Wu, Z. Wang, H. Qiang, G. Cai, C. Tan, C. Zhao, Detecting ripe fruits under natural occlusion and illumination conditions, Computers and Electronics in Agriculture 190, 106450 (2021). 10.1016/j.compag.2021.106450 [Google Scholar]
  8. X. Zhou, P. Wang, G. Dai, J. Yan, Z. Yang, Tomato fruit maturity detection method based on YOLOV4 and statistical color model, in 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CY-BER) (IEEE, 2021), pp. 904–908 [Google Scholar]
  9. D. Yang, C. Ju, Performance comparison of cherry tomato ripeness detection using multiple yolo models, AgriEngineering 7, 8 (2024). 10.3390/agriengineering7010008 [Google Scholar]
  10. P. Li, J. Zheng, P. Li, H. Long, M. Li, L. Gao, Tomato maturity detection and counting model based on mhsa-yolov8, Sensors 23 (2023). 10.3390/s23156701 [Google Scholar]
  11. P.J. Loresco, I. Valenzuela, R.P. Gamara, J.B. Obien, E. Dadios, Growth Stage Identi-fication for Cherry Tomato using Image Processing Techniques, in 2020 IEEE 12th In-ternational Conference on Humanoid, Nanotechnology, Information Technology, Com-munication and Control, Environment, and Management (HNICEM) (2020), pp. 1–6 [Google Scholar]
  12. Y.P. Huang, T.H. Wang, H. Basanta, Using fuzzy mask r-cnn model to auto-matically identify tomato ripeness, IEEE Access 8, 207672 (2020). 10.1109/AC-CESS.2020.3038184 [Google Scholar]
  13. I.T. Chen, H.Y. Lin, Detection, Counting and Maturity Assessment of Cherry Tomatoes using Multi-spectral Images and Machine Learning Techniques., in VISIGRAPP (5: VISAPP) (2020), pp. 759–766 [Google Scholar]
  14. S. Wang, J. Xiang, D. Chen, C. Zhang, A method for detecting tomato maturity based on deep learning, Applied Sciences 14, 11111 (2024). 10.3390/app142311111 [Google Scholar]
  15. S. Wang, H. Jiang, J. Yang, X. Ma, J. Chen, Z. Li, X. Tang, Lightweight tomato ripeness detection algorithm based on the improved rt-detr, Frontiers in Plant Science 15, 1415297 (2024). 10.3389/fpls.2024.1415297 [Google Scholar]
  16. C. Wang, C. Wang, L. Wang, J. Wang, J. Liao, Y. Li, Y. Lan, A lightweight cherry tomato maturity real-time detection algorithm based on improved yolov5n, Agronomy 13, 2106 (2023). 10.3390/agronomy13082106 [Google Scholar]
  17. J. Wei, L. Ni, L. Luo, M. Chen, M. You, Y. Sun, T. Hu, Gfs-yolo11: A maturity de-tection model for multi-variety tomato, Agronomy 14, 2644 (2024). 10.3390/agron-omy14112644 [Google Scholar]
  18. I. Sary, S. Andromeda, E. Armin, Performance comparison of yolov5 and yolov8 ar-chitectures in human detection using aerial images, Ultima Computing : Jurnal Sistem Komputer pp. 8–13 (2023). 10.31937/sk.v15i1.3204 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.