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- ItemEstimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner(Basel : MDPI, 2022) Saha, Kowshik Kumar; Tsoulias, Nikos; Weltzien, Cornelia; Zude-Sasse, ManuelaMonitoring of plant vegetative growth can provide the basis for precise crop manage-ment. 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 R2 = 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 cm3 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.
- ItemApple Shape Detection Based on Geometric and Radiometric Features Using a LiDAR Laser Scanner(Basel : MDPI, 2020) Tsoulias, Nikos; Paraforos, Dimitrios S.; Xanthopoulos, George; Zude-Sasse, ManuelaYield monitoring systems in fruit production mostly rely on color features, making the discrimination of fruits challenging due to varying light conditions. The implementation of geometric and radiometric features in three-dimensional space (3D) analysis can alleviate such difficulties improving the fruit detection. In this study, a light detection and range (LiDAR) system was used to scan apple trees before (TL) and after defoliation (TD) four times during seasonal tree growth. An apple detection method based on calibrated apparent backscattered reflectance intensity (RToF) and geometric features, capturing linearity (L) and curvature (C) derived from the LiDAR 3D point cloud, is proposed. The iterative discretion of apple class from leaves and woody parts was obtained at RToF > 76.1%, L < 15.5%, and C > 73.2%. The position of fruit centers in TL and in TD was compared, showing a root mean square error (RMSE) of 5.7%. The diameter of apples estimated from the foliated trees was related to the reference values based on the perimeter of the fruits, revealing an adjusted coefficient of determination (R2adj) of 0.95 and RMSE of 9.5% at DAFB120. When comparing the results obtained on foliated and defoliated tree’s data, the estimated number of fruit’s on foliated trees at DAFB42, DAFB70, DAFB104, and DAFB120 88.6%, 85.4%, 88.5%, and 94.8% of the ground truth values, respectively. The algorithm resulted in maximum values of 88.2% precision, 91.0% recall, and 89.5 F1 score at DAFB120. The results point to the high capacity of LiDAR variables [RToF, C, L] to localize fruit and estimate its size by means of remote sensing.