Estimation of Characteristic Parameters of Grape Clusters Based on Point Cloud Data

Abstract

The measurement of grapevine phenotypic parameters is crucial to quantify crop traits. However, individual differences in grape bunches pose challenges in accurately measuring their characteristic parameters. Hence, this study explores a method for estimating grape feature parameters based on point cloud information: segment the grape point cloud by filtering and region growing algorithm, and register the complete grape point cloud model by the improved iterative closest point algorithm. After estimating model phenotypic size characteristics, the grape bunch surface was reconstructed using the Poisson algorithm. Through the comparative analysis with the existing four methods (geometric model, 3D convex hull, 3D alpha-shape, and voxel-based), the estimation results of the algorithm proposed in this study are the closest to the measured parameters. Experimental data show that the coefficient of determination (R2) of the Poisson reconstruction algorithm is 0.9915, which is 0.2306 higher than the coefficient estimated by the existing alpha-shape algorithm (R2 = 0.7609). Therefore, the method proposed in this study provides a strong basis for the quantification of grape traits.

Comprehensive metabolomics-based analysis of sugar composition and content in berries of 18 grape varieties

“…The morphology and characteristics of berries vary among the varieties and different species of grapes (Table 4). Quantifying the phenotypic parameters of grape berries and bunches is important for precision agriculture (Liu et al, 2022a). The cultivated grapes are known to have high variation compared with wild resources, which largely resemble round berries in shape (Rodrıǵuez et al, 2011;Zhang et al, 2021).…”

Three-Dimensional Structure Measurement for Potted Plant Based on Millimeter-Wave Radar

“…As a stable alternative and complementary solution for perception tasks, millimeter-wave radar uses radio detection and ranging, and has gained attention in recent years [15]. Researchers have examined the use of millimeter-wave radar for various purposes in agriculture, such as guiding robots in smoky mazes [16], identifying subtle human actions [15], generating maps of orchards for automatic control [17], and building systems for predicting grape production [18] and assessing tomato sugar levels [19]. A new technique proposed by Nashashibi et al calculated the extinction and volume backscattering coefficients of different tree canopies under different physical conditions to improve the detection accuracy of millimeter-wave radar in plant canopies [20].…”