| Full text | |
| Author(s): |
da Silva, Matheus Ferreira
;
dos Santos, Renato Cesar
;
Gala, Mauricio
Total Authors: 3
|
| Document type: | Journal article |
| Source: | IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024; v. N/A, p. 4-pg., 2024-01-01. |
| Abstract | |
Fruit detection is an essential task for automatic crop forecasting and mechanization. In this context, LiDAR data acquired by terrestrial laser scanning (TLS) systems can be explored to perform fruit counting and size estimation, since this data presents a high geometric quality, and it is not affected by lighting conditions. This paper investigates the application of intensity information and geometric descriptors/features for orange fruit detection. In addition, we explore statistical graphical analysis, density clustering and filtering techniques for individual fruit segmentation. The results demonstrated the effectiveness of the proposed approach, achieving Fscore around 83% for orange fruit detection by combining intensity and planarity feature. (AU) | |
| FAPESP's process: | 24/04106-2 - 2024 IEEE International Geoscience and Remote Sensing Symposium - IGARSS |
| Grantee: | Renato César dos Santos |
| Support Opportunities: | Research Grants - Meeting - Abroad |
| FAPESP's process: | 21/06029-7 - High resolution remote sensing for digital agriculture |
| Grantee: | Antonio Maria Garcia Tommaselli |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 22/11647-4 - Multi-user equipment approved in grant 2021/06029-7: Terrestrial Laser Scanner FARO Focus Premium 70 |
| Grantee: | Antonio Maria Garcia Tommaselli |
| Support Opportunities: | Multi-user Equipment Program |