Open Access
Issue |
E3S Web of Conf.
Volume 488, 2024
1st International Conference on Advanced Materials & Sustainable Energy Technologies (AMSET2023)
|
|
---|---|---|
Article Number | 03015 | |
Number of page(s) | 9 | |
Section | Green Buildings; Carbon Capture & Recycling of Energy Materials | |
DOI | https://doi.org/10.1051/e3sconf/202448803015 | |
Published online | 06 February 2024 |
- FAO, “Restoring Coconut Farmers' livelihoods in the Philippines,” Food and Agriculture Organization of the United Nations, https:// www.fao.org/in- action/restoring-coconut-farmers-livelihoods-in-the-philippines/en/ (accessed Aug 5, 2023) [Google Scholar]
- R. Marasigan, E. Festijo and D. E. Juanico, Mangrove Crown Diameter Measurement from Airborne Lidar Data using Marker-controlled Watershed Algorithm: Exploring Performance. In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Kuala Lumpur, Malaysia, pp. 1-7, doi: 10.1109/ICETAS48360.2019.9117510 (2019) [Google Scholar]
- A. S. Alon, E. D. Festijo and D. E. O. Juanico, Tree Detection using Genus-Specific RetinaNet from Orthophoto for Segmentation Access of Airborne LiDAR Data. In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Kuala Lumpur, Malaysia, pp. 1-6, doi: 10.1109/ICETAS48360.2019.9117495 (2019) [Google Scholar]
- R. I. Marasigan, A. S. Alon, M. A. F. Malbog, J. N. Mindoro and S. G. Velasquez, Canarium Ovatum Recognition utilizing Mask R-CNN and Lightweight Unmanned Aerial Vehicle. In 2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC), Shah Alam, Malaysia, pp. 31-35, doi: 10.1109/ICSGRC55096.2022.9845172 (2022) [Google Scholar]
- Z. Zheng, J. Li, and L. Qin, Yolo-byte: An efficient multi-object tracking algorithm for automatic monitoring of Dairy Cows. Computers and Electronics in Agriculture, vol. 209, p. 107857, doi:10.1016/j.compag.2023.107857 (2023) [CrossRef] [Google Scholar]
- Y. Tian et al., MD-Yolo: Multi-scale Dense Yolo for small target pest detection. Computers and Electronics in Agriculture, vol. 213, p. 108233, doi:10.1016/j.compag.2023.108233 (2023) [CrossRef] [Google Scholar]
- F. Betti Sorbelli, L. Palazzetti, and C. M. Pinotti, Yolo-based detection of Halyomorpha Halys in orchards using RGB cameras and drones. Computers and Electronics in Agriculture, vol. 213, p. 108228, doi:10.1016/j.compag.2023.108228 (2023) [CrossRef] [Google Scholar]
- W. Xu et al., A lightweight SSV2-yolo based model for detection of sugarcane aphids in unstructured natural environments. Computers and Electronics in Agriculture, vol. 211, p. 107961, doi:10.1016/j.compag.2023.107961 (2023) [CrossRef] [Google Scholar]
- P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, A review of Yolo algorithm developments. Procedia Computer Science, vol. 199, pp. 1066–1073, doi:10.1016/j.procs.2022.01.135 (2022) [CrossRef] [Google Scholar]
- Solawetz, J. PP-Yolo surpasses Yolov4 - state of the Art Object Detection Techniques. Roboflow Blog. https://blog.roboflow.com/pp-yolo-beats-yolov4-object-detection/ (2022) [Google Scholar]
- CY Wang, A. Bochkovskiy and HM Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. (2022) [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.