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

Abstract

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.

Plant phenotyping relevance

3D LiDARとK-means分割により、樹上果実数・直径という植物器官形質を推定し、実測値との回帰・比較で技術検証しているため、フェノタイピング手法が中心である。

Code and data availability

The article describes LiDAR point-cloud acquisition, K-means fruit counting, and field yield measurements, but no public phenotype dataset, point clouds, images, analysis code, or supplement containing them is deposited or linked. The only URLs present are the PIAF lab site (cited prior work, Tree Analyser software of

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