| Texto completo | |
| Autor(es): |
da Silva, Matheus Ferreira
;
dos Santos, Renato Cesar
;
Gala, Mauricio
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024; v. N/A, p. 4-pg., 2024-01-01. |
| Resumo | |
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) | |
| Processo FAPESP: | 24/04106-2 - 2024 IEEE International Geoscience and Remote Sensing Symposium - IGARSS |
| Beneficiário: | Renato César dos Santos |
| Modalidade de apoio: | Auxílio à Pesquisa - Reunião - Exterior |
| Processo FAPESP: | 21/06029-7 - Sensoriamento remoto de alta resolução para agricultura digital |
| Beneficiário: | Antonio Maria Garcia Tommaselli |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 22/11647-4 - EMU concedido no processo 2021/06029-7: Laser Scanner Terrestre FARO Focus Premium 70 |
| Beneficiário: | Antonio Maria Garcia Tommaselli |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Equipamentos Multiusuários |