Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow

Gené-Mola, Jordi; Gregorio, Eduard; Cheein, Fernando Auat; Guevara, Javier; Llorens, Jordi; Sanz, Ricardo; Escolà, Alexandre; Rosell-Polo, Joan R.

doi:10.1016/j.compag.2019.105121

Cited by 72 publications

(38 citation statements)

References 59 publications

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“…The linear correlation equation between the measured and standard volume of potatoes was analyzed using SPSS. The R 2 value was found to be 1.000 with an RMSE of 1.80 cm 3 ; thus the measured surface volume was in good agreement with the actual volume. It is believed that the point cloud coordinates obtained with the device for this paper and the reconstructed 3D model are strongly correlated with the potato surface, whose pits and buds can be visualized.…”

Section: Discussionsupporting

confidence: 53%

“…where V is the corrected volume and V0 is the measured volume. The coefficient of determination is 1.000 and RMSE = 1.02 cm 3 . The average density of potatoes used for calibration was calculated to be 1.0805 g/cm 3 .…”

Section: A System Calibration Resultsmentioning

confidence: 94%

“…The regression model of volume was used for potato verification, and the volume and mass of the potato obtained using the point cloud were compared with those that were manually measured. The coefficient of determination between potato mass and volume was 1.000, with an average relative error of -0.08% and RMSE of 1.80 cm 3 . The potato mass could be accurately estimated with an average relative error of 0.48%.…”

Section: Discussionmentioning

confidence: 95%

“…By measuring the volume of cultivated crops or agricultural products, yields can be accurately calculated [1], [2]. Geometric characteristics and yield monitoring of crops can give farmers feedback [3] that can help them grade fruits and vegetables for added value [4], [5]. However, the analysis of phenotypic features is mostly artificial [6], which is labor-intensive and inefficient, resulting in a so-called "phenotyping bottleneck" [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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Measurement of Potato Volume With Laser Triangulation and Three-Dimensional Reconstruction

Cai

Jin

et al. 2020

IEEE Access

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Potato grading is related to weight. Three-dimensional (3D) reconstruction can provide highly accurate volume measurements of potatoes, which can help farmers to analyze their phenotypic characteristics and grade them. Considering their low cost and the required accuracy, a monocular camera and line laser were used to build a potato phenotype determination scanning device. The system obtains coordinates along the surface of a potato, collects laser light reflected from the surface in real time, and completes the coordinate calculation of the original points using the triangulation method. However, the original point clouds lose large areas of point clouds at the top and bottom of the potato. Point cloud repair is carried out by interpolation of points. In addition, the surface point cloud is smoothed. Finally, the generated point cloud is used for 3D reconstruction and volume calculation. In a volume error analysis test, potatoes are divided into calibration and verification groups. First, linear regression is used to relate the real and measured potato volume, and then the density of the potato is calculated. The volume and mass of potatoes in the verification group are measured by the device, and the standard volume and mass are measured manually. The results show that the average relative error in measured volume is −0.08%, and the average relative error in estimated mass is 0.48%. These results indicate that the combination of a line laser and a single camera provides accurate measurements of potato volume that can be used for yield estimation and potato grading.

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Section: Discussionsupporting

confidence: 53%

Section: A System Calibration Resultsmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Measurement of Potato Volume With Laser Triangulation and Three-Dimensional Reconstruction

Cai

Jin

et al. 2020

IEEE Access

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“…A terrestrial scanning system can map fruit distribution, detect and predict yield for an individual tree in an almond orchard (Underwood et al, 2016). Orchard inventory using a terrestrial scanning system was also developed for an apple orchard (Gené-Mola et al, 2020). 99% of apple trees were identified and some geometric parameters were determined with a sub-decimeter accuracy using high-density point cloud obtained with UAS (Hadas et al, 2019).…”

Section: * Corresponding Authormentioning

confidence: 99%

Identification of Peach Tree Trunks From Laser Scanning Data Obtained With Small Unmanned Aerial System

Hadas

Kölle

Karpina

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Agricultural robotics rely on digital tools and sensor integration in order to improve efficiency and sustainability of cultivations. One part of orchard inventory is the identification of a tree trunk i.e. localization and diameter determination. However, this is a challenging task, due to thin trunks, presence of leaves and low branches. In this paper we present a case study for determining these parameters using the example of peach orchard, for which a high-density LiDAR data (over 3000 points/m2) was obtained with a small unmanned aerial system (UAS) during a leafy and leafless season. We applied point thresholding by height and by components of normal vector, in order to identify points reflected from trunks. Alpha-shape algorithm was used to aggregate together points, that belong to the same trunk and their centroid determined the trunk location. Trunk diameters were calculated using two alternative approaches: the Principal Component Analysis (PCA) and circle fit. For the leafy season trunk identification is challenging. Omission errors were caused due to few reflections from trunks and commission errors occurred because of the unfiltered reflections from low branches and young twigs oriented towards the ground. All 194 trunks were identified from data collected during the leafless season. The accuracy of tree location was 0.27 m and the accuracy of diameter determination using PCA was 0.03 m.

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Sensing Crop Geometry and Structure

Gregorio

Llorens

2021

Sensing Approaches for Precision Agriculture

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Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow

Cited by 72 publications

References 59 publications

Measurement of Potato Volume With Laser Triangulation and Three-Dimensional Reconstruction

Measurement of Potato Volume With Laser Triangulation and Three-Dimensional Reconstruction

Identification of Peach Tree Trunks From Laser Scanning Data Obtained With Small Unmanned Aerial System

Sensing Crop Geometry and Structure

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