In-Field Estimation of Orange Number and Size by 3D Laser Scanning
The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geo...
| Main Authors: | , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7435 |
| _version_ | 1789406646780297216 |
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| author | Méndez, Valeriano Pérez Romero, Antonio Sola-Guirado, Rubén Miranda-Fuentes, Antonio Manzano Agugliar, Francisco Zapata Sierra, Antonio Jesús Rodríguez Lizana, Antonio |
| author_facet | Méndez, Valeriano Pérez Romero, Antonio Sola-Guirado, Rubén Miranda-Fuentes, Antonio Manzano Agugliar, Francisco Zapata Sierra, Antonio Jesús Rodríguez Lizana, Antonio |
| author_sort | Méndez, Valeriano |
| collection | DSpace |
| description | The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 ‘Salustiana’ variety orange trees—Citrus sinensis (L.) Osbeck—(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7435 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-74352023-04-12T19:32:31Z In-Field Estimation of Orange Number and Size by 3D Laser Scanning Méndez, Valeriano Pérez Romero, Antonio Sola-Guirado, Rubén Miranda-Fuentes, Antonio Manzano Agugliar, Francisco Zapata Sierra, Antonio Jesús Rodríguez Lizana, Antonio orange tree fruit recognition K-means LiDAR HDS GNSS yield estimation in-field The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 ‘Salustiana’ variety orange trees—Citrus sinensis (L.) Osbeck—(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate. 2020-01-16T12:52:43Z 2020-01-16T12:52:43Z 2019-12-13 info:eu-repo/semantics/article 2073-4395 http://hdl.handle.net/10835/7435 en https://www.mdpi.com/2073-4395/9/12/885 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | orange tree fruit recognition K-means LiDAR HDS GNSS yield estimation in-field Méndez, Valeriano Pérez Romero, Antonio Sola-Guirado, Rubén Miranda-Fuentes, Antonio Manzano Agugliar, Francisco Zapata Sierra, Antonio Jesús Rodríguez Lizana, Antonio In-Field Estimation of Orange Number and Size by 3D Laser Scanning |
| title | In-Field Estimation of Orange Number and Size by 3D Laser Scanning |
| title_full | In-Field Estimation of Orange Number and Size by 3D Laser Scanning |
| title_fullStr | In-Field Estimation of Orange Number and Size by 3D Laser Scanning |
| title_full_unstemmed | In-Field Estimation of Orange Number and Size by 3D Laser Scanning |
| title_short | In-Field Estimation of Orange Number and Size by 3D Laser Scanning |
| title_sort | in-field estimation of orange number and size by 3d laser scanning |
| topic | orange tree fruit recognition K-means LiDAR HDS GNSS yield estimation in-field |
| url | http://hdl.handle.net/10835/7435 |
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