LIDAR Soil Clod Detection Using Terrain Height Deviations
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Solution Overview
Problem
Existing soil analysis methods for detecting soil clods in agricultural fields are time-consuming and expensive, and there is a need for a more efficient system to monitor and adjust soil roughness to ensure healthy field conditions for planting operations.
Innovation Solution
A system and method using field sensors, such as lidar systems, to capture data and a computing system to generate terrain plots and identify soil clods based on height deviations from a reference line, allowing for the detection and potential adjustment of soil clods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional soil analysis methods are used to detect soil clods, then measurement precision can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces traditional mechanical/manual soil analysis methods with an optical sensing system using a lidar sensor mounted on an agricultural vehicle. The lidar sensor emits laser pulses and measures the time of flight to detect terrain variations and identify soil clods automatically, eliminating the need for time-consuming manual field analysis while maintaining detection accuracy
Solution Approach 2:
The patent introduces a computing system as an intermediary that processes lidar sensor data to generate terrain plots and automatically identifies soil clods by analyzing height deviations from reference lines. This intermediary system automates the detection process, reducing both time and cost compared to traditional methods
2Loss of information
If traditional soil analysis methods are used, then comprehensive soil condition data can be obtained, but the cost increases significantly
Solution Approach 1:
The patent replaces expensive traditional soil sampling and laboratory analysis with a cost-effective lidar-based optical sensing system that can be mounted on standard agricultural vehicles, significantly reducing the cost of obtaining comprehensive soil condition data
Solution Approach 2:
The patent makes the agricultural vehicle itself a multi-functional platform that performs both field operations and soil condition monitoring, eliminating the need for separate specialized analysis equipment and reducing overall system cost
3Measurement precision
If manual soil clod detection is performed, then accurate identification can be achieved, but productivity decreases
Solution Approach 1:
The patent enables continuous real-time detection of soil clods as the agricultural vehicle moves through the field, rather than requiring intermittent manual sampling. The lidar sensor continuously scans the terrain and the computing system continuously processes data to identify soil clods, maintaining both accuracy and high productivity
Solution Approach 2:
The system performs self-service detection by automatically capturing lidar data, generating terrain plots, identifying soil clods through algorithmic analysis of height deviations, and providing real-time feedback without requiring manual intervention, thereby maintaining accuracy while maximizing productivity
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 efficient and accurate detection of soil clods, reducing the time and cost associated with traditional analysis methods, and facilitating proactive adjustments to maintain optimal soil conditions for agricultural operations.
Implementation Method 1
a lidar system to capture images of a region of the field. A computing system may be configured to receive the captured images from the lidar system
Implementation Method 2
field sensors, such as lidar systems, to capture data
Data Source
AI summary
A methods for soil clod detection within a field is provided herein and can include receiving, with a computing system, data indicative of terrain variations within a region of an agricultural field. The region of the field is comprised of one or more adjacently positioned segments. The method can also include generating, with the computing system, a mean reference line. The method can further include calculating, with the computing system, a segment height for each of the one or more adjacently positioned segments. The method can also include determining, with the computing system, a presence of an object based on a deviation of one of the one or more segment heights being greater than a threshold height from the reference line.