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Monitoring of plant vegetative growth can provide the basis for precise crop management. In this study, a 2D light detection and ranging (LiDAR) laser scanner, mounted on a linear conveyor, was used to acquire multi-temporal three-dimensional (3D) data from strawberry plants (���Honeoye��� and ���Malling Centenary���) 14���77 days after planting (DAP). Canopy geometrical variables, i.e., points per plant, height, ground projected area, and canopy volume profile, were extracted from 3D point cloud. The manually measured leaf area exhibited a linear relationship with LiDAR-derived parameters (R2 = 0.98, 0.90, 0.93, and 0.96 with number of points per plant, volume, height, and projected canopy area, respectively). However, the measuring uncertainty was high in the dense canopies. Particularly, the canopy volume estimation was adapted to the plant habitus to remove gaps and empty spaces in the canopy point cloud. The parametric values for maximum point to point distance (Dmax) = 0.15 cm and slice height (S) = 0.10 cm resulted in R�� = 0.80 and RMSPE = 26.93% for strawberry plant volume estimation considering actual volume measured by water displacement. The vertical volume profiling provided growth data for cultivars ���Honeoye��� and ���Malling Centenary��� being 51.36 cm�� at 77 DAP and 42.18 cm3 at 70 DAP, respectively. The results contribute an approach for estimating plant geometrical features and particularly strawberry canopy volume profile based on LiDAR point cloud for tracking plant growth.

The capacity of apple trees to produce fruit of a desired diameter, i.e., fruit-bearing capacity (FBC), was investigated by considering the inter-tree variability of leaf area (LA). The LA of 996 trees in a commercial apple orchard was measured by using a terrestrial two-dimensional (2D) light detection and ranging (LiDAR) laser scanner for two consecutive years. The FBC of the trees was simulated in a carbon balance model by utilizing the LiDAR-scanned total LA of the trees, seasonal records of fruit and leaf gas exchanges, fruit growth rates, and weather data. The FBC was compared to the actual fruit size measured in a sorting line on each individual tree. The variance of FBC was similar in both years, whereas each individual tree showed different FBC in both seasons as indicated in the spatially resolved data of FBC. Considering a target mean fruit diameter of 65 mm, FBC ranged from 84 to 168 fruit per tree in 2018 and from 55 to 179 fruit per tree in 2019 depending on the total LA of the trees. The simulated FBC to produce the mean harvest fruit diameter of 65 mm and the actual number of the harvested fruit >65 mm per tree were in good agreement. Fruit quality, indicated by fruit's size and soluble solids content (SSC), showed enhanced percentages of the desired fruit quality according to the seasonally total absorbed photosynthetic energy (TAPE) of the tree per fruit. To achieve a target fruit diameter and reduce the variance in SSC at harvest, the FBC should be considered in crop load management practices. However, achieving this purpose requires annual spatial monitoring of the individual FBC of trees.

Eine genaue Schätzung der geometrischen Pflanzenvariablen in Obstanlagen ist unerlässlich für deren präzise Bewirtschaftung. Auf dem Traktor montierte, mobile 2D-LiDAR-Systeme können die Aufnahme von 3D-Punktewolken der Oberflächen und somit eine Analyse des Wachstums von Obstbäumen in der Zukunft ermöglichen. Obwohl die Methode einige Vorteile hat, wie die Verwendung einer aktiven Lichtquelle zur Messung bei wechselnden Lichtverhältnissen, kann jedoch eine Verzerrung der 3D-Punktewolke aufgrund des störenden Einflusses von Reichweite und Einfallswinkel des Laserstrahls auftreten. Um diese Effekte zu quantifizieren, wurde ein Quader mit bekannter Größe vermessen. Die Oberfläche des Metallkastens wurde mit Bariumsulfat als Diffusionsbeschichtung modifiziert. Die Ergebnisse weisen darauf hin, dass die geschätzte mittlere Höhe des Kastens kleiner als die tatsächliche Höhe war, während die Breitenschätzung die tatsächliche Breite überschritt. Der höchste Fehler (RMSE) betrug 2,09 % bzw. 0,91 % für die Höhen- und Breitenschätzung. Es wurde festgestellt, dass die Schätzung des Volumens des Kastens und anschließend eines Obstbaums unter Verwendung von segmentierten Ebenen des Messobjektes zur Vermessung gut geeignet war und in kommerziellen Obstanlagen neue Möglichkeiten der Informationsgewinnung für die präzise, einzelbaumspezifische Bewirtschaftung bietet., LANDTECHNIK, Bd. 75 Nr. 4 (2020)

This paper describes an approach to estimate the photosynthetic capacity and derive the optimum fruit number for each individual tree, in order to achieve a defined fruit size, which is named as the fruit bearing capacity of the tree. The estimation of fruit bearing capacity was carried out considering the total leaf area per tree as measured with a 2-D LiDAR laser scanner, LALiDAR, and key carbon-related variables of the trees including leaf gas exchange, fruit growth and respiration, in two commercial apple orchards. The range between minLALiDAR and maxLALiDAR was found to be 2.4 m² on Pinova and 4.3 m² on RoHo 3615 at fully developed canopy. The daily C requirement of the growing fruit and the associated leaf area demand, necessary to meet the average daily fruit C requirements showed seasonal variation, with maximum values in the middle of the growing period. The estimated fruit bearing capacity ranged from 33-95 fruit tree-1 and 45-121 fruit tree-1 on the trees of Pinova and RoHo 3615, respectively. This finding demonstrates sub-optimal crop load at harvest time in both orchards, above or below the fruit bearing capacity for individual trees. In conclusion, the LiDAR measurements of the leaf area combined with a carbon balance model allows for the estimation of fruit bearing capacity for individual trees for precise crop load management.