LiDAR Residue Coverage Sensing Corrected by Soil Moisture
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Solution Overview
Problem
Existing systems for determining residue coverage in agricultural fields are inadequate, as they do not accurately account for variations in soil moisture content, which affects light reflectivity and can lead to inaccurate residue coverage measurements.
Innovation Solution
A system and method utilizing LiDAR sensors and soil moisture sensors to determine residue coverage by analyzing light reflectivity and soil moisture content, enabling precise calculation of residue coverage values and controlling ground-engaging tools based on these measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If LiDAR sensor is used to measure light reflectivity for residue coverage determination, then measurement capability is improved, but measurement precision deteriorates due to soil moisture variations affecting reflectivity
Solution Approach 1:
The patent introduces soil moisture sensors as an intermediary element that measures soil moisture content separately. This measurement serves as a correction factor to compensate for the effect of soil moisture on light reflectivity. By using the soil moisture data to adjust the reflectivity-based residue coverage calculations, the system eliminates the measurement error caused by moisture variations, thereby improving measurement precision while maintaining the ability to detect residue coverage.
2Measurement precision
If multiple sensors (LiDAR and soil moisture sensors) are integrated, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent integrates multiple sensors (LiDAR and soil moisture sensors) into a single agricultural implement, making the system multi-functional. The LiDAR sensor measures light reflectivity for residue coverage determination, while the soil moisture sensors measure soil moisture content for correction purposes. Both sensors work together within the same system framework, with their data processed by a unified control system that calculates corrected residue coverage values. This multi-functional integration improves measurement precision without proportionally increasing complexity, as the sensors share common mounting, power, and data processing infrastructure.
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
Accurately determines residue coverage without requiring significant computing resources, allowing for optimized agricultural operations by adjusting tool penetration depth and speed based on residue coverage.
Implementation Method 1
a LiDAR sensor configured to emit light-based output signals for reflection off of a field surface of a portion of the field and detect reflections of the light-based output signals as return signals
Data Source
AI summary
A system for determining residue coverage of a field includes a LiDAR sensor configured to emit light-based output signals for reflection off of a field surface of a portion of a field and detect reflections of the light-based output signals as return signals. Furthermore, the system includes a soil moisture sensor configured to generate soil moisture sensor data indicative of a soil moisture content of the portion of the field. Additionally, a computing system is configured to receive LiDAR sensor data associated with the return signals detected by the LiDAR sensor. Moreover, the computing system is configured to determine a soil moisture value for the portion of the field based on the soil moisture sensor data. In addition, the computing system is configured to determine a residue coverage value of the portion of the field based on the determined soil moisture value and the LiDAR sensor data.