Harvest Path Prediction for Coordinated Grain Transfer
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Solution Overview
Problem
Efficiently coordinating the rendezvous of mobile agricultural work machines, such as harvesters and material receiving machines, to minimize downtime and optimize harvesting operations is challenging due to difficulties in scheduling material transfers and planning optimal routes and locations, leading to increased costs and reduced crop quality.
Innovation Solution
A system that generates operation plans using historical and current data to determine machine routes, sub-operation locations, and control signals for mobile agricultural work machines, optimizing the coordination of harvesters, grain carts, and grain trailers to ensure seamless material transfer and minimize downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual coordination of mobile agricultural work machines is used, then flexibility in operation is maintained, but downtime increases and operational efficiency decreases
Solution Approach 1:
The system performs preliminary action by generating operation plans and predicting initial harvest paths before the harvesting operation begins. The operation plan includes predetermined machine routes, sub-operation locations, and transfer points that are calculated in advance based on historical data and current worksite conditions, allowing machines to execute coordinated actions without real-time delays
Solution Approach 2:
The system implements feedback by continuously monitoring current operation data and comparing it with historical operation data to refine and update operation plans during execution. The machine learning model learns from past operations and adjusts future predictions based on actual performance, creating a closed-loop system that improves efficiency while maintaining flexibility
2Productivity
If complex scheduling algorithms are implemented to optimize material transfers, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by introducing a machine learning model that acts as a mediator between historical data and current operation planning. Instead of implementing complex real-time scheduling algorithms, the system employs a trained model that predicts optimal initial harvest paths and operation plans based on patterns learned from historical data, simplifying the decision-making process while maintaining high operational efficiency
Solution Approach 2:
The system applies copying by creating a virtual model of the worksite and operation based on historical data. The machine learning model generates predicted operation plans that replicate successful patterns from past operations, allowing the system to optimize material transfers and machine coordination without requiring complex real-time calculations
3Measurement precision
If historical operation data is collected and analyzed, then prediction accuracy improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary action by collecting and processing historical operation data in advance to train the machine learning model before actual harvesting operations begin. This preprocessing phase creates a trained model that can quickly generate predictions during operations without requiring intensive real-time data processing, thus reducing energy requirements during critical harvesting periods
Data Source
AI summary
A computer implemented method includes obtaining historical operation data relative to a plurality of historical operations, the historical operation data including historical machine data, historical worksite data, historical productivity data, and historical logistics data; obtaining current operation data relative to an underway or upcoming operation, the current operation data including current machine data and current worksite data; generating, based on the obtained historical operation data and the obtained current operation data, an operation plan output relative to the underway or upcoming operation, the operation plan output including one or more of: (i) one or more machine routes; (ii) one or more sub-operation locations; (iii) one or more operation plan maps; or a combination of (i), (ii), and (iii); and generating control signals to control one or more mobile agricultural work machines based on the operation plan output.