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.
Highlights
The estimation of a crop in its different growth stages is essential when making decisions about harvest, storage, transport, and marketing
Machine vision systems are crucial for automatic fruit detection, the challenges of which were suggested by Sarig [1], as it is a more intuitive approach
This study opens new perspectives for alternatives, such as photogrammetry-SFM and automatic image detection techniques based on Machine Learning, but always taking, as a reference, the information generated with the 3D laser scanner
Summary
The estimation of a crop in its different growth stages is essential when making decisions about harvest, storage, transport, and marketing. In the case of fruit, this estimation is commonly based on manual counts, but they are time-consuming, expensive, and come with huge errors. It is necessary to search for automatic counting alternatives, and it is an option to count from information obtained with a camera or a sensor such as LiDAR. Machine vision systems are crucial for automatic fruit detection, the challenges of which were suggested by Sarig [1], as it is a more intuitive approach. The fruit detection must be able to be carried out under different environmental conditions and with the restrictions of shade by leaves, branches, and other immature fruits.
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