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In-Field Estimation of Orange Number and Size by 3D Laser Scanning

Department of Applied Mathematics, E.T.S.I.A, Polytechnic University of Madrid, Ciudad Universitaria, Av. Puerta de Hierro, 2, 28040 Madrid, Spain
Department of Graphic Engineering, E.T.S.I.A., University of Sevilla, Utrera Road, km 1, 41013 Sevilla, Spain
Department of Rural Engineering, Campus Rabanales, University of Cordoba, Ed. Leonardo da Vinci, Ctra. Nacional IV, km 396, 14014 Córdoba, Spain
Department of Aerospace Engineering and Fluids Mechanics, Area of Rural Engineering, E.T.S.I.A., University of Seville, Utrera Road, km. 1, 41013 Seville, Spain
Department of Engineering, ceiA3, University of Almeria, 04120 Almeria, Spain
Author to whom correspondence should be addressed.
Agronomy 2019, 9(12), 885;
Received: 2 November 2019 / Revised: 10 December 2019 / Accepted: 11 December 2019 / Published: 13 December 2019
(This article belongs to the Special Issue In-Field Estimation of Fruit Quality and Quantity)
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. View Full-Text
Keywords: orange tree; fruit recognition; K-means; LiDAR; HDS; GNSS; yield estimation; in-field orange tree; fruit recognition; K-means; LiDAR; HDS; GNSS; yield estimation; in-field
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MDPI and ACS Style

Méndez, V.; Pérez-Romero, A.; Sola-Guirado, R.; Miranda-Fuentes, A.; Manzano-Agugliaro, F.; Zapata-Sierra, A.; Rodríguez-Lizana, A. In-Field Estimation of Orange Number and Size by 3D Laser Scanning. Agronomy 2019, 9, 885.

AMA Style

Méndez V, Pérez-Romero A, Sola-Guirado R, Miranda-Fuentes A, Manzano-Agugliaro F, Zapata-Sierra A, Rodríguez-Lizana A. In-Field Estimation of Orange Number and Size by 3D Laser Scanning. Agronomy. 2019; 9(12):885.

Chicago/Turabian Style

Méndez, Valeriano; Pérez-Romero, Antonio; Sola-Guirado, Rubén; Miranda-Fuentes, Antonio; Manzano-Agugliaro, Francisco; Zapata-Sierra, Antonio; Rodríguez-Lizana, Antonio. 2019. "In-Field Estimation of Orange Number and Size by 3D Laser Scanning" Agronomy 9, no. 12: 885.

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