3D Point Cloud Plant Location Detection Under Canopy Masking
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Solution Overview
Problem
Identifying plant locations in agricultural terrains is challenging due to the masking effect of plant canopies, requiring complex image processing to distinguish plant stems from the background.
Innovation Solution
A method using a 3D point cloud analysis to identify plant locations by defining a reference plane relative to the agricultural machine's coordinate system, dividing it into regions, and applying height and point distribution conditions to define plant volumes, simplifying the processing and accurately identifying plant locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image processing is used to identify plant stems through the canopy, then plant location identification accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential geometric features (3D point cloud coordinates, height, and spatial distribution) needed for plant location identification, discarding the complex visual processing of canopy images. This extraction approach maintains identification accuracy while significantly reducing processing complexity by focusing only on the critical stem location data.
Solution Approach 2:
The patent replaces complex optical/image processing systems with a simpler 3D spatial analysis system using point cloud data. By substituting visual recognition with geometric analysis of 3D coordinates, the system achieves the same identification function with reduced computational complexity.
2Measurement precision
If 3D point cloud analysis with multiple conditions is applied, then plant location accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the plant identification process into distinct spatial conditions: height condition (vertical extent), distribution condition (spatial arrangement), and volume condition (3D occupancy). By dividing the analysis into these independent geometric segments, the system can efficiently evaluate each condition separately rather than processing the entire point cloud as a single complex problem.
Solution Approach 2:
The patent changes the analysis parameters from 2D image pixel values to 3D spatial parameters (coordinates, height, volume, density). This parameter transformation enables more efficient geometric computations that maintain high accuracy while reducing processing time compared to iterative image analysis methods.
Data Source
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AI summary
According to a method of identifying plant locations within a terrain, a 3D point cloud (600) is received representing those points, in a 3D volume extending above the terrain, where solid matter is present. Regions of a reference plane of said 3D volume are identified for which solid matter aligned in a height direction with said reference plane region meets a first, height, condition. Volumes extending in the height direction relative to those identified regions are defined and volumes which meet a second, point distribution, condition are identified as extending above the plant locations.