Drone-Based Three-Dimensional Photogrammetry and Concave Hull by Slices Algorithm for Apple Tree Volume Mapping

Dong, Xuhua; Kim, Woo Young; Lee, Kyeong–Hwan

doi:10.1007/s42853-021-00120-y

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…However, some of the unique challenges during UAV data collection include weather limitations (e.g., wind, rain), inconsistent image quality due to tree movement, and fewer image details due to high flight altitudes. Nonetheless, when weather allows, commercial miniature UAVs that are robust and economical demonstrate much potential in being employed by growers for orchard management [47][48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

UAV Photogrammetry-Based Apple Orchard Blossom Density Estimation and Mapping

Yuan¹,

Hua

Heinemann

et al. 2023

Horticulturae

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Thinning is an important routine for apple growers to manage crop load and improve fruit quality, which can be accomplished through manual, chemical, or mechanical manipulation of flowers and fruitlets. Traditionally, blossom thinning relies on human experts’ visual evaluation of the flower load, a leading indicator of crop load, which can be imprecise and prone to errors. This study aimed to develop an apple blossom density mapping algorithm utilizing point clouds reconstructed through unmanned aerial vehicle (UAV)-based red-green-blue (RGB) imagery and photogrammetry. The algorithm was based on grid average downsampling and white color thresholding, and it was able to generate top-view blossom density maps of user-defined tree height regions. A preliminary field experiment was carried out to evaluate the algorithm’s accuracy using manual blossom counts of apple tree row sections as ground truths, and a coefficient of determination (R2) of 0.85, a root mean square error (RMSE) of 1307, and a normalized RMSE (NRMSE) of 9.02% were achieved. The algorithm was utilized to monitor the blooming of the apple tree rows and was demonstrated to effectively show blossom density variations between different tree rows and dates. The study results suggested the potential of UAVs as a convenient tool to assist precise blossom thinning in apple orchards, while future research should further investigate the reliability of photogrammetry techniques under different image qualities and flight settings as well as the influence of blossom distribution on algorithm accuracy.

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Section: Introductionmentioning

confidence: 99%

UAV Photogrammetry-Based Apple Orchard Blossom Density Estimation and Mapping

Yuan¹,

Hua

Heinemann

et al. 2023

Horticulturae

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“…Similarly, in oblique photography, the width of the sensor (s w ), the focal length in the x direction (f x ), the distance (D) from the object, and the forward overlap (a f ) are used to determine the interval (D m ) between waypoints along the strip, as given by Equation (5).…”

Section: Automatic Path Planning For Flightmentioning

confidence: 99%

“…The introduction of virtual reality and augmented reality technologies has increased the demand for three-dimensional (3D) models [1][2][3][4][5][6][7][8]. Among the various 3D modeling techniques, unmanned aerial vehicle (UAV) photogrammetry has the advantages of time and cost efficiency [1,[4][5][6][7][8][9][10][11][12][13].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Development and Validation of Unmanned Aerial Vehicle Photogrammetry Simulator for Shaded Area Detection

Kim

Kim²,

Jeon³

et al. 2022

Applied Sciences

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In unmanned aerial vehicle (UAV) photogrammetry, the qualities of three-dimensional (3D) models, including ground sample distance (GSD) and shaded areas, are strongly affected by flight planning. However, during flight planning, the quality of the output cannot be estimated, as it depends on the experience of the operator. Therefore, to reduce the time and cost incurred by repetitive work required to obtain satisfactory quality, a simulator, which can automatically generate a route, acquire images through simulation, and analyze the shaded areas without real flight, has been required. While some simulators have been developed, there are some limitations. Furthermore, evaluating the performance of the simulator is difficult owing to the lack of a validation method. Therefore, to overcome the limitations, target functions, which can plan flights and can detect shaded areas, were set, developed, and validated in this study. As a result, a simulator successfully planned a flight and detected shaded areas. In this way, the simulator was validated to determine the applicability of its performance. Furthermore, the outputs of this study can be applied to not only UAV photogrammetry simulators but also other 3D modeling simulators.

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