Harvester Row Guidance With Context Sensing in Non-Crop Areas
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Solution Overview
Problem
Agricultural harvesters struggle to differentiate between crop plants and weeds or grass using tactile-based sensors, leading to undesired steering when encountering non-crop areas, such as drainage or harvested fields.
Innovation Solution
Implementing a control system that utilizes contextual inputs from sensors like GPS, optical cameras, and operator inputs to determine when to ignore row sensing data and switch to manual steering, ensuring accurate alignment with crop rows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If tactile-based row sensors are used to guide steering, then the harvester can automatically follow crop rows, but the harvester cannot differentiate between crop plants and weeds/grass leading to undesired steering in non-crop areas
Solution Approach 1:
The patent combines multiple sensing modalities (tactile row sensors, optical cameras, GPS positioning) into an integrated context sensing system. The optical sensor detects visual characteristics of plants while GPS provides location data, and tactile sensors detect physical contact. By merging these data sources, the system can differentiate between crop rows and non-crop areas like weeds or drainage ditches, resolving the reliability issue while maintaining automation.
Solution Approach 2:
The patent introduces an intermediary processing layer (context sensing module) that mediates between the raw sensor inputs and the steering control. This intermediary analyzes contextual information from multiple sensors to determine whether detected objects are actual crop rows or false positives (weeds, grass in drainage areas), preventing undesired steering corrections while maintaining automatic operation.
2Productivity
If the harvester follows row sensor signals in non-crop areas, then continuous automatic guidance is maintained, but the harvester steers toward non-existent rows causing inefficient operation
Solution Approach 1:
The system performs preliminary context analysis using optical sensors and GPS positioning before acting on row sensor signals. By pre-validating whether detected rows are legitimate crop rows or false positives, the system prevents unnecessary manual intervention while maintaining productivity in valid crop areas.
Solution Approach 2:
The patent implements feedback mechanisms where the context sensing system continuously monitors and validates row sensor signals. When false rows are detected (through optical verification or GPS location mismatch with field maps), the system provides feedback to suppress erroneous steering commands, maintaining automatic operation efficiency without following non-existent rows.
Data Source
AI summary
A computer-implemented method of operating an agricultural work machine is provided. The method includes initiating row sensing guidance for the agricultural work machine to guide steering of the agricultural work machine based at least one signal from a row sensor; obtaining contextual information; determining whether a row is present based on the contextual information; and selectively ignoring the at least one signal from the row sensor based on whether a row is present. An agricultural work machine and a control system for an agricultural work machine are also provided.