A calculation method of phenotypic traits based on three-dimensional reconstruction of tomato canopy

Zhu, Tianyu; Ma, Xiaodan; Guan, Haibing; Wu, Xiaohu; Wang, Feiyi; Yang, Chen; Qiu, Jianhui

doi:10.1016/j.compag.2022.107515

Cited by 28 publications

(7 citation statements)

References 30 publications

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“…This study utilized the AlphaShape algorithm to estimate the LVV value for individual trees. The AlphaShape algorithm is a geometric shape reconstruction algorithm used for processing point cloud data [44]. It can be used to control the smoothness and level of detail of shapes based on the parameter alpha.…”

Section: Extract Living Vegetation Volumementioning

confidence: 99%

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

Yang,

Shen,

Cao

2024

Remote Sensing

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The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O2 production and CO2 absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m3), followed by the sycamore tree species (557.907 m3), and privet’s was the lowest (16.681 m3).

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Section: Extract Living Vegetation Volumementioning

confidence: 99%

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

Yang,

Shen,

Cao

2024

Remote Sensing

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“…In this study, we set the parameter α to a constant of 0.05 to exclude smaller pores within the canopy and contain as much detailed structural information as possible without creating discontinuity within the 3D contour. Various studies have already used The AS algorithm for 3D reconstruction, canopy extraction, LA, and crown biomass estimation [50,51].…”

Section: Volume Calculation With Tls Datamentioning

confidence: 99%

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

Li,

Hu,

Xing

et al. 2024

Remote Sensing

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Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.

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“…Initially, the operator identified and highlighted a mature tomato in the 3D reconstructed picture using the graphical user interface. The location 𝑃(𝑥, 𝑦, 𝑧) of the tomato item in three dimensions was determined using the method of triangulation, as described by Zhu et al [41]. This calculation was based on the two pictures obtained by the stereo camera.…”

Section: Vision Systemmentioning

confidence: 99%

Field Performance of a Dual Arm Robotic System for Efficient Tomato Harvesting

Lem,

Mackey

2024

JRS

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The robot device that is being addressed in this research has two arms: one for picking the fruit and the other for chopping it. The arms find and locate pods with the help of a complex vision system that employs cameras. In this human-robot workflow, the operator chooses the tomatoes they want picked, and then the robot does the actual picking. The robot management and communication system use the EtherCAT bus to create a link with the graphical user interface (GUI), enabling human administration and control. The objective of this project is to create and assess a robotic system for harvesting tomatoes, equipped with dual arms. This system incorporates a mobile model equipped with two robotic arms and an end effector to enhance the efficiency of tomato harvesting. The system uses a GUI to enhance interaction between the robot and the human operator. Additionally, it employs a vision model to streamline the process of fruit detection. Findings from this study demonstrate that HMI may significantly improve the accuracy of tomato harvesting robots. Finally, there were some difficulties in developing 3D models because this study included outdoor experiments.

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A calculation method of phenotypic traits based on three-dimensional reconstruction of tomato canopy

Cited by 28 publications

References 30 publications

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

Field Performance of a Dual Arm Robotic System for Efficient Tomato Harvesting

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