![]() ![]() (2020) estimated the individual leaf area of tomato using the polygon area of plant surface models constructed by a 3D scanner. (2011) estimated the leaf area and LAI of tomato using the polygon area of a canopy surface model scanned from three places by the LIDAR. The use of single-image 2D analysis to estimate growth parameters was affected by imaging position, plant density, and species compared with the use of a 3D analysis. (2013) estimated the plant height, leaf area, and fresh weight of corn and cotton using solid models constructed by 3D stereovision modeling. (2015) estimated the canopy height of a deciduous forest using a 3D multispectral point cloud acquired by unmanned aerial vehicle-structure from motion (UAV-SFM) remote sensing. In addition, the solid model has been used to predict the volume of trees. Hosoi and Omasa (2006) estimated the leaf area density (LAD) and LAI of trees using solid models constructed by a LIDAR, which precisely reproduced the canopy. Casadesús and Villegas (2014) estimated the LAI and dry weight of wheat by calculating the ratio of green pixels from multiple images. (2011) extracted soybean leaves from a 2D image, including the background, using the hue, saturation, intensity (HSI) color model, and calculated the leaf area using the number of leaf pixels. The point cloud is a set of data points comprising coordinates in a space. (2007) constructed 3D structural models of barley leaves and stems using triangles calculated by 3D point cloud data to extract morphological traits. Recently, the growth of trees and grains has been estimated using two- (2D) or three-dimensional (3D) information.ĭornbusch et al. Therefore, appropriate leaf thinning increases the yield of fruit vegetables hence, the technique used to monitor the growth of fruit vegetables should also consider the timing of leaf thinning. Higashide (2018) showed that when the leaf area index (LAI) was increased, the amount of solar radiation in the lower canopy was lower than the light compensation point, meaning that it could not contribute to photosynthesis due to the consumption of assimilation products by respiration. Monitoring the growth of fruit vegetables will provide input data that can be used to control robots for cultivation management and harvesting. In the future, there will be a need to automate harvesting and cultivation management in order to save energy. In greenhouse horticulture, environmental control (for example, temperature, solar radiation, CO 2 and vapor-pressure deficit ) systems have been developed using information and communication technology (ICT). ![]() Farmers continue to seek methods to increase productivity using small numbers of individuals. It is vital to increase the efficiency of agricultural work because of the high labour cost, highlighting the need for automation. Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner. A significant correlation was observed between the measured and estimated fruit weights (tomato: R 2 = 0.739, paprika: R 2 = 0.888). The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable thus, the dry weight of the plant can be predicted using the estimated leaf area. The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R 2 > 0.8, except for tomato LAI). We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. ![]()
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