Please use this identifier to cite or link to this item:
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1136667Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | SANTOS, T. T. | |
| dc.contributor.author | GEBLER, L. | |
| dc.date.accessioned | 2021-11-26T12:00:39Z | - |
| dc.date.available | 2021-11-26T12:00:39Z | - |
| dc.date.created | 2021-11-26 | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 13., 2021, Bagé. Anais [...]. Bagé: Unipampa, 2021. | |
| dc.identifier.isbn | 978-65-00-34526-1 | |
| dc.identifier.issn | 2177-9724 | |
| dc.identifier.uri | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1136667 | - |
| dc.description | Abstract. Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available. | |
| dc.language.iso | eng | |
| dc.rights | openAccess | eng |
| dc.subject | Redes neurais | |
| dc.subject | Contagem automática de frutas | |
| dc.subject | Detecção de maçãs | |
| dc.subject | Convolutional neural networks | |
| dc.subject | Fruit detection | |
| dc.title | A methodology for detection and localization of fruits in apples orchards from aerial images. | |
| dc.type | Artigo em anais e proceedings | |
| dc.subject.thesagro | Maçã | |
| dc.subject.nalthesaurus | Neural networks | |
| dc.subject.nalthesaurus | Apples | |
| dc.description.notes | Organizado por Ana Paula Lüdtke Ferreira. SBIAgro 2021. | |
| dc.format.extent2 | p. 1-9. | |
| riaa.ainfo.id | 1136667 | |
| riaa.ainfo.lastupdate | 2021-11-26 | |
| dc.contributor.institution | THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV. | |
| Appears in Collections: | Artigo em anais de congresso (CNPTIA)  | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| PL-Methodology-detection-localization-SBIAgro-2021.pdf | 4,14 MB | Adobe PDF |  View/Open |
