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- ItemCarbon consumption of developing fruit and the fruit bearing capacity of individual RoHo 3615 and Pinova apple trees(Lublin : IA PAS, 2020) Penzel, Martin; Lakso, Alan Neil; Tsoulias, Nikos; Zude-Sasse, ManuelaThis 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 m on Pinova and 4.3 m 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. © 2020 Polish Academy of Sciences. All rights reserved.
- ItemIn-situ fruit analysis by means of LiDAR 3D point cloud of normalized difference vegetation index (NDVI)(Amsterdam [u.a.] : Elsevier, 2023) Tsoulias, Nikos; Saha, Kowshik Kumar; Zude-Sasse, ManuelaA feasible method to analyse fruit at the tree is requested in precise production management. The employment of light detection and ranging (LiDAR) was approached aimed at measuring the number of fruit, quality-related size, and ripeness-related chlorophyll of fruit skin. During fruit development (65 – 130 day after full bloom, DAFB), apples were harvested and analysed in the laboratory (n = 225) with two LiDAR laser scanners measuring at 660 and 905 nm. From these two 3D point clouds, the normalized difference vegetation index (NDVILiDAR) was calculated. The correlation analysis of NDVILiDAR and chemically analysed fruit chlorophyll content showed R2 = 0.81 and RMSE = 3.63 % on the last measuring date, when fruit size reached 76 mm. The method was tested on 3D point clouds of 12 fruit trees measured directly in the orchard, during fruit growth on five measuring dates, and validated with manual fruit analysis in the orchard (n = 4632). Point clouds of individual apples were segmented from 3D point clouds of trees and fruit NDVILiDAR were calculated. The non-invasively obtained field data showed good calibration performance capturing number of fruit, fruit size, fruit NDVILiDAR, and chemically analysed chlorophyll content of R2 = 0.99, R2 = 0.98 with RMSE = 3.02 %, R2 = 0.65 with RMSE = 0.65 %, R2 = 0.78 with RMSE = 1.31 %, respectively, considering the related reference data at last measuring date 130 DAFB. The new approach of non-invasive laser scanning provided physiologically and agronomically valuable time series data on differences in fruit chlorophyll affected by the leaf area to number of fruit and leaf area to fruit fresh mass ratios. Concluding, the method provides a tool for gaining production-relevant plant data for, e.g., crop load management and selective harvesting by harvest robots.
- ItemGrowth Height Determination of Tree Walls for Precise Monitoring in Apple Fruit Production Using UAV Photogrammetry(Basel : MDPI, 2020) Hobart, Marius; Pflanz, Michael; Weltzien, Cornelia; Schirrmann, MichaelIn apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for.