EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 69 (2024) ITM Web Conf., 69 (2024) 04017 References
Open Access
Issue
ITM Web Conf.
Volume 69, 2024
International Conference on Mobility, Artificial Intelligence and Health (MAIH2024)
Article Number 04017
Number of page(s) 8
Section Transactions
DOI https://doi.org/10.1051/itmconf/20246904017
Published online 13 December 2024
  1. Zarboubi, Mohamed, Samira Chabaa, and Azzedine Dliou. "Advancing Precision Agriculture with Deep Learning and IoT Integration for Effective Tomato Pest Management." 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). IEEE, 2023. [Google Scholar]
  2. Sarkozi, A. "New standards to curb the global spread of plant pests and diseases." Food and Agriculture Organization of the United Nations: Roma, Italy (2019). [Google Scholar]
  3. Karar, Mohamed Esmail, et al. "A new mobile application of agricultural pests recognition using deep learning in cloud computing system." Alexandria Engineering Journal 60.5 (2021): 4423–4432. [CrossRef] [Google Scholar]
  4. Son, A-R., S-J. Suh, and Y-M. Park. "Notes on insects captured in codling moth (Lepidoptera: Tortri- cidae) surveillance traps in South Korea over the last eight years." EPPO Bulletin 48.3 (2018): 578–582. [CrossRef] [Google Scholar]
  5. Jiang, Dong, et al. "Mapping the potential global codling moth (Cydia pomonella L.) distribution based on a machine learning method." Scientific reports 8.1 (2018): 13093. [CrossRef] [Google Scholar]
  6. Cirjak, Dana, et al. "Automatic pest monitoring systems in apple production under changing climatic conditions." Horticulturae 8.6 (2022): 520. [CrossRef] [Google Scholar]
  7. Suto, Jozsef. "Improving the generalization capability of YOLOv5 on remote sensed insect trap images with data augmentation." Multimedia Tools and Applications 83.9 (2024): 27921–27934. [Google Scholar]
  8. Suto, Jozsef. "Codling moth monitoring with camera- equipped automated traps: A review." Agriculture 12.10 (2022): 1721. [CrossRef] [Google Scholar]
  9. Beers, Elizabeth H., David R. Horton, and Eugene Miliczky. "Pesticides used against Cydia pomonella disrupt biological control of secondary pests of apple." Biological Control 102 (2016): 35–43. [CrossRef] [Google Scholar]
  10. Sun, Yu, et al. "Automatic in-trap pest detection using deep learning for pheromone-based Dendroc- tonus valens monitoring." Biosystems engineering 176 (2018): 140–150. [CrossRef] [Google Scholar]
  11. Høye, Toke T., et al. "Deep learning and computer vision will transform entomology." Proceedings of the National Academy of Sciences 118.2 (2021): e2002545117. [CrossRef] [Google Scholar]
  12. Zarboubi, Mohamed, et al. "Smart Pest Control in Grain Warehouses: YOLOv8-powered IoT Robot Car for Precision Agriculture." 2024 International Conference on Global Aeronautical Engineering and Satellite Technology (GAST). IEEE, 2024. [Google Scholar]
  13. Guo, Qingwen, et al. "Automatic monitoring of flying vegetable insect pests using an RGB camera and YOLO-SIP detector." Precision Agriculture 24.2 (2023): 436–457. [CrossRef] [Google Scholar]
  14. Kiobia, Denis O., et al. "A review of successes and impeding challenges of IoT-based insect pest detection systems for estimating agroecosystem health and productivity of cotton." Sensors 23.8 (2023): 4127. [CrossRef] [Google Scholar]
  15. Qing, Jiajun, et al. "Intelligently Counting Agricultural Pests by Integrating SAM with FamNet." Applied Sciences 14.13 (2024): 5520. [CrossRef] [Google Scholar]
  16. Dong, Shifeng, et al. "ESA-Net: An efficient scaleaware network for small crop pest detection." Expert Systems with Applications 236 (2024): 121308. [CrossRef] [Google Scholar]
  17. Gao, Yufan, et al. "Intelligent field monitoring system for cruciferous vegetable pests using yellow sticky trap images and an improved Cascade R-CNN." Journal of Integrative Agriculture (2024). [Google Scholar]
  18. Karar, Mohamed Esmail, et al. "A new mobile application of agricultural pests recognition using deep learning in cloud computing system." Alexandria Engineering Journal 60.5 (2021): 4423–4432. [CrossRef] [Google Scholar]
  19. Wang, Tiewei, et al. "Recognition and counting of typical apple pests based on deep learning." Ecological Informatics 68 (2022): 101556. [CrossRef] [Google Scholar]
  20. Li, Wenyong, et al. "Classification and detection of insects from field images using deep learning for smart pest management: A systematic review." Ecological Informatics 66 (2021): 101460. [CrossRef] [Google Scholar]
  21. Li, Kangshun, et al. "A fast and lightweight detection algorithm for passion fruit pests based on improved YOLOv5." Computers and Electronics in Agriculture 204 (2023): 107534. [CrossRef] [Google Scholar]
  22. Xu, Weiyue, et al. "A lightweight SSV2-YOLO based model for detection of sugarcane aphids in unstructured natural environments." Computers and Electronics in Agriculture 211 (2023): 107961. [CrossRef] [Google Scholar]
  23. Bellout, Abdelaaziz, et al. "Deep Learning technique for predicting tomato leaf disease." 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). IEEE, 2023. [Google Scholar]
  24. Kalfas, Ioannis, et al. "Towards automatic insect monitoring on witloof chicory fields using sticky plate image analysis." Ecological Informatics 75 (2023): 102037. [CrossRef] [Google Scholar]
  25. Chen, Chao, et al. "An automatic inspection system for pest detection in granaries using YOLOv4." Computers and Electronics in Agriculture 201 (2022): 107302. [CrossRef] [Google Scholar]
  26. Zhu, Linqi, et al. "Research on CBF-YOLO detection model for common soybean pests in complex environment." Computers and Electronics in Agriculture 216 (2024): 108515. [CrossRef] [Google Scholar]
  27. Zhao, Chao, et al. "AC-YOLO: Multi-category and high-precision detection model for stored grain pests based on integrated multiple attention mechanisms." Expert Systems with Applications (2024): 124659. [CrossRef] [Google Scholar]
  28. Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [Google Scholar]
  29. Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [Google Scholar]
  30. Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018). [Google Scholar]
  31. Bochkovskiy, Alexey, Chien-Yao Wang, and HongYuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934 (2020). [Google Scholar]
  32. Jocher, Glenn, et al. "ultralytics/yolov5: v3. 0." Zenodo (2020). [Google Scholar]
  33. Li, Chuyi, et al. "YOLOv6: A single-stage object detection framework for industrial applications." arXiv preprint arXiv:2209.02976 (2022). [Google Scholar]
  34. Wang, Chien-Yao, Alexey Bochkovskiy, and HongYuan Mark Liao. "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023. [Google Scholar]
  35. G. Jocher, A. Chaurasia, and J. Qiu, “YOLO by Ultralytics.” https://github.com/ultralytics/ultralytics, 2023. Accessed: August 15, 2023. [Google Scholar]
  36. Wang, Chien-Yao, I-Hau Yeh, and Hong-Yuan Mark Liao. "Yolov9: Learning what you want to learn using programmable gradient information." arXiv preprint arXiv:2402.13616 (2024). [Google Scholar]
  37. Wang, Ao, et al. "Yolov10: Real-time end-to- end object detection." arXiv preprint arXiv:2405.14458 (2024). [Google Scholar]
  38. Wang, Jiangning, et al. "A new automatic identification system of insect images at the order level." Knowledge-Based Systems 33 (2012): 102–110. [CrossRef] [Google Scholar]
  39. Yalcin, Hulya. "Vision based automatic inspection of insects in pheromone traps." 2015 Fourth International Conference on Agro-Geoinformatics (Agrogeoinformatics). IEEE, 2015. [Google Scholar]
  40. Bakkay, Mohamed Chafik, et al. "Automatic detection of individual and touching moths from trap images by combining contour-based and region-based segmentation." IET Computer Vision 12.2 (2018): 138–145. [CrossRef] [Google Scholar]
  41. Wen, Chenglu, and Daniel Guyer. "Image-based orchard insect automated identification and classification method." Computers and electronics in agriculture 89 (2012): 110–115. [CrossRef] [Google Scholar]
  42. Liu, Huajian, Sang-Heon Lee, and Javaan Singh Chahl. "A review of recent sensing technologies to detect invertebrates on crops." Precision Agriculture 18 (2017): 635–666. [CrossRef] [MathSciNet] [Google Scholar]
  43. Sütő, József. "Embedded system-based sticky paper trap with deep learning-based insect-counting algorithm." Electronics 10.15 (2021): 1754. [CrossRef] [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.