Stereo Point Cloud Filtering for Accurate Foliage Density Estimation
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Solution Overview
Problem
Smart spraying systems face challenges in accurately detecting foliage density, leading to inaccurate chemical application, which can result in environmental harm and increased costs due to over- or under-spraying.
Innovation Solution
A method utilizing a stereo camera system with a semantic detector and point cloud filtering to differentiate foliage components, generate a region of interest, and estimate foliage density, enabling precise chemical application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If traditional sprayers apply chemicals uniformly across entire fields, then coverage is ensured, but chemical waste increases and environmental impact worsens
Solution Approach 1:
The system applies different spray quantities to different locations based on detected foliage density. Areas with high foliage density receive more chemical, while areas with low density receive less or no chemical, eliminating uniform application waste while maintaining effective coverage where needed.
Solution Approach 2:
The patent replaces traditional mechanical uniform-spray systems with an automated system using sensors (cameras, LiDAR, ultrasonic sensors) and computer vision to detect foliage density and control spray nozzles accordingly, enabling precision spraying that reduces chemical waste while maintaining reliability.
2Measurement precision
If sensors overestimate foliage density, then spray quantity increases, but chemical waste and environmental harm increase
Solution Approach 1:
The system combines multiple sensor types (cameras for visual detection, LiDAR for 3D structure, ultrasonic sensors for distance) and integrates their data through fusion algorithms to achieve more accurate foliage density measurements, reducing both over- and under-estimation errors that lead to environmental harm.
Solution Approach 2:
The system continuously monitors foliage density and adjusts spray quantity in real-time based on sensor feedback, allowing dynamic adaptation to actual conditions and preventing both over-spraying (which causes environmental harm) and under-spraying (which reduces effectiveness).
3Loss of substance
If sensors underestimate foliage density, then chemical application decreases, but spray effectiveness reduces and yields decrease
Solution Approach 1:
By merging data from multiple sensor modalities (visual, 3D structural, acoustic) and using fusion algorithms, the system achieves more accurate foliage density detection that prevents under-estimation, ensuring sufficient chemical application for effective spray while avoiding unnecessary chemical usage.
4Shape
If LiDAR is used in dense foliage, then 3D structure detection improves, but signal attenuation increases and data accuracy decreases
Solution Approach 1:
The system combines LiDAR data with camera images and ultrasonic sensor measurements to compensate for LiDAR signal attenuation in dense foliage. The fusion algorithm integrates complementary information from all sensors to maintain accurate foliage density measurement even when LiDAR signals are blocked or attenuated.
Solution Approach 2:
The system uses camera images and ultrasonic sensor data as intermediary information to infer foliage density in areas where LiDAR signals are attenuated, acting as mediators that bridge the gap between LiDAR limitations and accurate measurement requirements.
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
Enables accurate and efficient delivery of chemicals by identifying relevant foliage areas, reducing waste and environmental impact while optimizing chemical usage.
Implementation Method 1
capturing a point cloud using a stereo camera
Implementation Method 2
differentiating, using a semantic detector, material components from a two-dimensional image
Implementation Method 3
producing a filtered point cloud by: filtering the point cloud using the filter
Implementation Method 4
generating a material density estimate, for the material components, using the filtered point cloud
Data Source
AI summary
Systems and methods related to performing material density estimates are disclosed herein. A stereo imaging system may include a stereo camera and one or more processors. The stereo imaging system may capture a point cloud using the stereo camera, determine a region of interest using the point cloud, differentiate material components from a two-dimensional image to produce a filter, produce a filtered point cloud by (i) filtering the point cloud using the filter; and (ii) excluding points from the point cloud using the region of interest, and generate a material density estimate, for the material components, using the filtered point cloud. The material density estimate may allow the system to perform appropriate actions. For example, the material density estimate may allow the system to spray an appropriate amount of chemical on crops, reducing detrimental environmental effects, reducing costs, and improving yields.