Voxel-Normal Wheat Canopy Leaf Angle Distribution
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Solution Overview
Problem
Current methods for estimating leaf inclination angle distribution in wheat canopies lack automatic segmentation for curved leaves and result in uneven leaf point density, making them time-consuming and labor-intensive or requiring extensive post-processing.
Innovation Solution
A method using a voxel segmentation normal vector algorithm to divide the wheat canopy into voxels, calculate normal vectors, and average angles within each voxel to estimate leaf inclination angle distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement methods (inclinometer, protractor) are used to obtain leaf inclination angle distribution, then measurement precision is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces mechanical measurement tools (inclinometer, protractor) with a terrestrial laser scanner that uses optical/laser fields to capture 3D point cloud data of the canopy structure, enabling automated extraction of leaf inclination angles without manual contact measurement
Solution Approach 2:
The patent creates a digital 3D copy (point cloud model) of the physical canopy structure from laser scanning data, allowing virtual analysis of leaf inclination angles through computational algorithms without physically measuring each leaf
2Measurement precision
If direct measurement methods are used to obtain leaf inclination angle distribution, then measurement precision is improved, but labor intensity increases significantly
Solution Approach 1:
The patent replaces manual mechanical measurement operations with automated laser scanning and computational algorithms that process 3D point cloud data to extract leaf inclination angle distributions without human intervention
Solution Approach 2:
The system performs self-service by automatically processing the scanned data through voxel segmentation and normal vector calculation algorithms to generate leaf inclination angle distributions without requiring manual analysis or interpretation
3Loss of time
If conventional terrestrial laser scanning methods are used to estimate leaf inclination angle distribution, then time consumption is reduced, but automatic segmentation for curved leaves is lacking and leaf point density is uneven
Solution Approach 1:
The patent segments the 3D canopy point cloud into discrete voxels (3D pixels), allowing independent processing of each voxel to calculate normal vectors and determine leaf inclination angles, which enables accurate segmentation of curved leaves while maintaining uniform point density
Solution Approach 2:
The patent applies local quality by calculating normal vectors and inclination angles specifically for each voxel based on the local point cloud distribution within that voxel, allowing accurate representation of curved leaf surfaces while maintaining consistent processing across the entire canopy
4Ease of operation
If simplified mathematical functions are used to characterize leaf inclination angle distribution, then ease of operation is improved, but adaptability to different species and growth stages deteriorates
Solution Approach 1:
The patent creates a digital 3D copy of the actual canopy structure from laser scanning data, preserving the true geometric characteristics of leaves across different species and growth stages, rather than relying on simplified mathematical models that cannot capture biological variability
Solution Approach 2:
The system automatically adapts to different species and growth stages by directly measuring the actual canopy structure through laser scanning and voxel segmentation, eliminating the need for manual selection or adjustment of species-specific mathematical parameters
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides an efficient, automated estimation of leaf inclination angle distribution with good correlation to measured values, supporting high-throughput phenotyping by accounting for different cultivars, growth stages, and nitrogen levels.
Implementation Method 1
Terrestrial laser scanning is an active remote sensing technology that emits laser pulses to a spherical space around the scanner and generate a point cloud of a target object by using the time of flight of the emitted pulses to and from the target object
Data Source
AI summary
A method for automatically estimating a leaf inclination angle distribution of a wheat canopy based on a voxel segmentation normal vector algorithm, including the following steps: step 1: obtaining point cloud data of a wheat canopy; step 2: splicing and denoising point clouds; step 3: calculating normal vectors of the point clouds; step 4: performing voxelization on the point clouds; step 5: segmenting the normal vectors by using a voxel; step 6: calculating angles of the voxels; step 7: collecting statistics on the angles of the voxels and performing curve fitting calculation to obtain a leaf inclination angle distribution and an average leaf inclination angle. The average leaf inclination angle estimated by the method is compared with field measured data, and the feasibility of the algorithm is verified by using a three-dimensional radiative transfer model.