Harvester Path Planning Using Predictive Field Productivity Maps
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Solution Overview
Problem
Coordinating the various operations and movements during agricultural harvesting is challenging, affecting the overall productivity of the harvest system.
Innovation Solution
A system and method for determining field productivity using a predictive productivity index module to identify harvesting and non-harvesting portions of a field, generating a predictive productivity map, and guiding agricultural harvesters based on field features and potential paths to optimize harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional harvesting coordination methods are used, then operational simplicity is maintained, but harvesting productivity and throughput are reduced
Solution Approach 1:
The system performs preliminary actions by predicting future transport availability and calculating productivity indices for different field portions before the harvester reaches them. The predictive productivity map is generated in advance, allowing the harvester to be guided to optimal locations based on predicted rather than current conditions, thereby improving productivity without requiring complex real-time coordination.
Solution Approach 2:
The controller acts as an intermediary between the harvester and transport vehicles, using predictive algorithms to mediate coordination. Rather than direct complex communication between all vehicles, the controller processes transport availability data and translates it into guidance instructions for the harvester, simplifying the overall system architecture while improving productivity.
2Loss of time
If harvester moves continuously without optimization, then operational simplicity is maintained, but time loss during non-harvesting operations increases
Solution Approach 1:
The system calculates productivity indices and generates the predictive map in advance, identifying optimal harvesting locations before the harvester arrives. This preliminary analysis allows the harvester to minimize non-harvesting time by directly navigating to high-productivity areas without complex real-time path adjustments.
Solution Approach 2:
The field is divided into portions with different productivity indices, and the harvester is guided to specific high-value locations rather than harvesting uniformly across the entire field. This local optimization reduces travel time to low-productivity areas while the guidance system manages the complexity of selective path planning.
3Productivity
If transport availability is not considered, then operational coordination is simplified, but harvesting throughput is reduced
Solution Approach 1:
The system predicts transport availability in advance and incorporates this information into the productivity index calculation. By knowing future transport conditions beforehand, the system can plan harvesting locations that align with predicted transport availability, improving throughput without requiring complex real-time coordination between harvester and transport vehicles.
Solution Approach 2:
The system uses feedback from transport availability data to adjust the predictive productivity map. Transport vehicle status and availability information are fed back into the system, which then recalculates optimal harvesting locations, creating a closed-loop coordination mechanism that improves throughput while managing complexity through automated feedback processing.
Data Source
AI summary
One or more techniques and/or systems are disclosed for improving harvest productivity by determining a harvest productivity index for a plurality of areas of a field. Path planning is performed for the one or more harvester vehicles based on the determined harvest productivity index for the plurality of areas of the field and the determined out of field crop transport vehicle availability. One or more routes of the path planning are adjusted in response to changes in harvest operation.