Open Access
| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02051 | |
| Number of page(s) | 8 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802051 | |
| Published online | 17 November 2023 | |
- V. Puri, A. Nayyar, and L. Raja, “Agriculture drones: A modern breakthrough in precision agriculture,” J. Stat. Manag. Syst., vol. 20, no. 4, pp. 507–518, 2017. [Google Scholar]
- Marsujitullah, Z. Zainuddin, S. Manjang, and A. S. Wijaya, “Rice Farming Age Detection Use Drone Based on SVM Histogram Image Classification,” J. Phys. Conf. Ser., vol. 1198, no. 9, 2019. [Google Scholar]
- Fauzan Masykur; Kusworo Adi; Oky Dwi Nurhayati, “Classification of Paddy Leaf Disease Using MobileNet Model,” in 8th International Conference on Computing, Engineering and Design (ICCED), 2022. [Google Scholar]
- A. Hafeez et al., “Implementation of drone technology for farm monitoring & pesticide spraying: A review,” Inf. Process. Agric., no. xxxx, 2022. [Google Scholar]
- I. Wahab, O. Hall, and M. Jirström, “Remote sensing of yields: Application of UAV imagery-derived ndvi for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa,” Drones, vol. 2, no. 3, pp. 1–16, 2018. [Google Scholar]
- F. Masykur, K. Adi, and O. D. Nurhayati, “Approach and Analysis of Yolov4 Algorithm for Rice Diseases Detection at Different Drone Image Acquisition Distances,” TEM J., vol. 12, no. 2, pp. 928–935, 2023. [CrossRef] [Google Scholar]
- A. Triwahyudin, H. K. Safitri, and M. Fauziyah, “Pembacaan Jarak dan Kecepatan dengan ArUco Marker pada Sistem Koper Follow Me Beroda,” Maj. Ilm. Teknol. Elektro, vol. 21, no. 1, p. 97, 2022. [CrossRef] [Google Scholar]
- W. Fitrian Roshandri, E. Utami, and A. Budi Prasetyo, “Diabetes Wound Perimeter Analysis Using Pixel Per Metric,” Sisfotenika, vol. 12, no. 2, pp. 156–169, 2022. [Google Scholar]
- T. Tocci, L. Capponi, and G. Rossi, “ArUco marker-based displacement measurement technique: Uncertainty analysis,” Eng. Res. Express, vol. 3, no. 3, 2021. [Google Scholar]
- X. T. and B. W. B. Li, J. Wu, “ArUco Marker Detection under Occlusion Using Convolutional Neural Network,” vol. 8, 2020. [Google Scholar]
- E. R. JAMZURI, R. ANALIA, and S. SUSANTO, “Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 11, no. 1, p. 128, 2023. [Google Scholar]
- F. J. Romero-Ramirez, R. Muñoz-Salinas, and R. Medina-Carnicer, “Speeded up detection of squared fiducial markers,” Image Vis. Comput., vol. 76, pp. 38–47, 2018. [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.