Crop-Row Point Cloud Control for GPS-Weak Implement Guidance
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Solution Overview
Problem
Existing agricultural vehicle implement control systems lack precision and efficiency, particularly in environments with weak GPS signals or dense vegetation, leading to inefficient use of resources like power and spraying liquid.
Innovation Solution
Utilizing a point cloud sensor to generate a point cloud of the crop row, determining vertical point density distributions within defined crop row volumes, and controlling the implement based on these distributions to optimize resource use and adapt to local crop growth conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based implement control system is used, then positioning accuracy is sufficient for general operations, but reliability deteriorates in environments with weak GPS signals such as dense vegetation or greenhouses
Solution Approach 1:
The patent introduces LIDAR as an intermediary sensing system that operates independently of GPS signals. The LIDAR system scans the crop row and generates point cloud data that serves as a mediator to determine implement position and control implement operation, bypassing the need for GPS signal reception in environments with weak satellite coverage.
2Productivity
If uniform spraying liquid application is used, then implementation is simple, but efficiency deteriorates due to wasted power and spraying liquid on areas with less dense vegetation
Solution Approach 1:
The patent applies local quality by varying the spraying liquid application rate based on local crop row density characteristics. The system divides the crop row into multiple volumes, determines point density for each volume, and adjusts spraying liquid flow rate accordingly - higher density areas receive more liquid while lower density areas receive less, optimizing resource efficiency.
3Measurement precision
If detailed vertical point density analysis is performed, then implement control precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the scanned crop row into multiple discrete volumes along the row length. For each volume, the system independently determines point density by counting points within that specific volume. This segmentation approach enables precise local measurements while maintaining manageable computational complexity through modular processing of individual volume data.
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
Enhances implement control precision and efficiency by optimizing vehicle power and spraying liquid use based on local crop density, even in environments with weak GPS signals, reducing waste and energy consumption.
Implementation Method 1
a point cloud sensor (20) to scan a portion of the crop row and to generate a point cloud
Data Source
AI summary
A method is provided for controlling an implement (50) of an agricultural vehicle (10) driving over a path (P) along a crop row (LR, RR). The method comprises using a point cloud sensor (20) to scan a portion of the crop row (LR, RR) and to generate a point cloud. A plurality of crop row volumes (91-98) is determined, arranged along the scanned portion of the crop row (LR, RR). For each of the crop row volumes (91-98), a vertical point density distribution is determined by calculating which points of the point cloud fall within said crop row volume (91-98). The implement (50) is controlled based on the vertical point density distribution of each of the crop row volumes (91-98).