Harvester Fill Level Prediction for Timely Grain Cart Rendezvous
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Solution Overview
Problem
Current combine harvesters face inefficiencies due to difficulty in predicting when the clean grain tank will be full and determining the optimal location for rendezvous with haulage units, leading to idle time and suboptimal deployment of harvesting machines and haulage units.
Innovation Solution
An agricultural harvesting machine equipped with a fill level sensor and a path processing system that generates a georeferenced probability metric based on predicted crop yield and field data, allowing for controlled operation to optimize path and speed to ensure timely rendezvous with haulage units and reduce idle time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If combine harvesters operate without real-time yield data and fill level prediction, then device complexity is reduced, but productivity decreases due to idle time waiting for haulage units
Solution Approach 1:
The system performs preliminary actions by obtaining predicted crop yield data for field segments before the harvester reaches them, and calculating georeferenced probability metrics in advance to predict where the repository will become full. This allows the harvester to plan its path and speed proactively rather than reactively, improving productivity without requiring complex real-time control systems.
Solution Approach 2:
The system implements feedback by using actual yield data collected during harvesting to correct the predicted crop yield, then using this corrected data to update the georeferenced probability metric. This continuous feedback loop refines predictions in real-time, enabling better decision-making about path and speed selection while maintaining manageable system complexity through incremental updates.
2Loss of time
If multiple haulage units and harvesters operate without coordinated information, then ease of operation is improved, but loss of time increases due to mismatched rendezvous
Solution Approach 1:
The system introduces an intermediary layer of information processing that converts raw yield data and repository fill level information into georeferenced probability metrics. These metrics serve as a common language that coordinates between harvesters and haulage units, enabling them to synchronize their operations and reduce idle time without requiring direct complex communication between all units.
Solution Approach 2:
The system segments the field into discrete field segments and calculates predicted crop yield for each segment individually. This segmentation allows the georeferenced probability metric to be computed for specific locations, enabling precise path planning and rendezvous coordination between multiple harvesters and haulage units, thereby reducing idle time through targeted information delivery.
3Loss of time
If the harvester travels at maximum speed without path optimization, then speed is improved, but loss of time increases due to premature repository fullness
Solution Approach 1:
The system applies dynamics by selecting path and speed combinations that are adaptive to the predicted crop yield distribution. Rather than maintaining a fixed maximum speed, the harvester dynamically adjusts its speed and path based on the georeferenced probability metric, traveling faster through low-yield segments and slower through high-yield segments, thereby optimizing total harvesting time rather than just instantaneous speed.
Data Source
AI summary
An agricultural harvesting machine comprises a path processing system that obtains a predicted crop yield at a plurality of different field segments along a harvester path on a field, and obtains field data corresponding to one or more of the field segments generated based on sensor data as the agricultural harvesting machine is performing a crop processing operation. A yield correction factor is generated based on the received field data and the predicted crop yield at the one or more field segments. Based on applying the yield correction factor to the predicted crop yield, a georeferenced probability metric is generated indicative of a probability that the harvested crop repository will reach the fill capacity at a particular geographic location along the field. A control signal generator generates a control signal to control the agricultural harvesting machine based on the georeferenced probability metric.