Residue Coverage Mapping Using Yield and UAV Sensor Feedback
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Solution Overview
Problem
Current methods for determining residue coverage after a harvesting operation are inaccurate due to uneven distribution of crop residue, which affects subsequent farming practices such as tillage and fertilization.
Innovation Solution
A system and method using yield data from sensors associated with harvesters and unmanned aerial vehicles (UAVs) to generate an estimated residue coverage map, which is then updated with residue data from sensors on the harvester or UAVs post-harvest, providing a more accurate representation of residue distribution across the field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If residue coverage is estimated based on yield data alone assuming even distribution, then the estimation process is simple, but the measurement precision is poor
Solution Approach 1:
The patent combines yield data from yield sensors with actual residue coverage data from residue sensors to create an updated residue coverage map. This merging of multiple data sources improves measurement precision by correcting the assumptions of even distribution with actual measured values, while the integrated system manages complexity through unified data processing.
Solution Approach 2:
The system uses residue sensors to measure actual residue coverage and feeds this information back to update the estimated residue coverage map generated from yield data. This feedback loop continuously improves measurement precision by comparing estimated versus actual values and adjusting the model accordingly.
2Measurement precision
If yield data from multiple sources is collected and processed to generate residue coverage maps, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system integrates multiple sensor types (yield sensors and residue sensors) and multiple data sources (harvester-based and UAV-based) into a single multi-functional platform that performs both yield monitoring and residue coverage mapping, improving precision while managing complexity through unified architecture.
Solution Approach 2:
The computing device acts as an intermediary that receives, processes, and integrates data from multiple heterogeneous sources (yield sensors, residue sensors, UAVs, harvesters) to generate the updated residue coverage map, managing the complexity of multi-source data integration through centralized processing.
3Measurement precision
If actual residue data from sensors is collected post-harvest, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The system collects yield data during the harvesting operation itself, performing preliminary estimation of residue coverage before actual residue measurement is needed. This allows the framework to be ready for rapid updating when residue sensor data becomes available, reducing the time penalty of precise measurement.
Solution Approach 2:
The system maintains continuous monitoring capabilities with both yield sensors operating during harvest and residue sensors ready to capture data immediately post-harvest, ensuring the useful action of data collection continues without interruption and minimizing time loss between harvest and residue assessment.
Data Source
AI summary
A method for determining residue coverage within a field after a harvesting operation may include receiving yield data associated with an estimated crop yield across a field and generating an estimated residue coverage map for the field based at least in part on the yield data. The method may further include receiving residue data associated with residue coverage across a surface of the field following the performance of a harvesting operation within the field. Additionally, the method may include generating an updated residue coverage map for the field based at least in part on the estimated residue coverage map and the residue data.