Abstract
Crop phenotyping data collection is the basis for precision agriculture and smart decision-making applications. Accurately obtaining the canopy volume of citrus trees is crucial for yield prediction, precise fertilization and cultivation management. To this end, we developed a dynamic slicing and reconstruction (DR) algorithm based on 3D point clouds. The algorithm dynamically slices nearby slices based on their proportional area change and density difference; for each slice point cloud, the average distance of each point from others is taken as the initial α value for the AS algorithm. This value is iteratively summed until it reconstructs the complete shape, allowing the volume of each slice shape to be determined. Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84) and exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees. The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal.
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Claims 3
Claim · model-written summary
The DR method achieved the best removal of perforations and lacunae among six reconstruction algorithms, at 0.84.
- Outcome
- removing perforations and lacunaee0
- Direction
- beste0
- Value
- 0.84e0
- Comparator
- six point cloud-based reconstruction algorithmse0
CitrusOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84)”
t0“Citrus Tree”
Claim · model-written summary
The DR method exhibited volumetric consistency of 1.53 aligned with citrus growth patterns.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees”
Claim · model-written summary
The DR method improved slice adaptability and reconstruction detail, noise reduction, and anomaly removal.
- Outcome
- improved adaptive ability, finer volume computations, better noise reduction, and anomaly removale0
- Comparator
- ASBS and CHBS algorithmse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal.”
t0“Citrus Tree”