Predictive Harvester Transfer Routing for Crop Moisture Segregation
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Solution Overview
Problem
Current agricultural harvesting systems face inefficiencies in managing crop characteristics, leading to increased costs and production delays due to improper mixing and drying of crops with varying moisture and quality levels.
Innovation Solution
A computer-implemented method that uses a predictive model and map to optimize the routing and material transfer operations between harvesters and receiving machines, based on crop characteristic thresholds, allowing for precise control of material transfer start and end locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If crops with varying moisture and quality levels are harvested together without differentiation, then harvesting speed increases, but crop quality standards cannot be met and waste increases
Solution Approach 1:
The field is divided into multiple zones based on crop characteristic maps (moisture content, quality levels). The harvester and receiving machines are assigned to specific zones, creating segmented harvesting operations that maintain both speed and quality control by processing similar crops together.
Solution Approach 2:
Different receiving machines are assigned to different field zones based on crop quality characteristics. Each receiving machine handles crops with specific moisture and quality ranges, ensuring local quality standards are met while maintaining overall harvesting efficiency.
2Productivity
If material transfer operations are performed continuously without optimization, then harvesting productivity is maintained, but fuel consumption and operational costs increase
Solution Approach 1:
The system pre-plans material transfer operations by generating optimized routes for receiving machines based on predicted crop characteristics and harvester locations. This preliminary routing minimizes unnecessary travel and transfer operations, reducing fuel consumption while maintaining productivity.
Solution Approach 2:
The system continuously monitors crop characteristics, harvester position, and receiving machine status to dynamically adjust material transfer schedules. This feedback mechanism optimizes transfer operations in real-time, eliminating wasteful fuel consumption while preserving harvesting productivity.
3Device complexity
If crop mixing is allowed to simplify handling, then operational complexity decreases, but quality standards cannot be met and drying requirements increase
Solution Approach 1:
Crops are segmented into different quality groups that are handled separately through dedicated receiving machines. This segmentation maintains quality standards by preventing mixing of crops with different moisture and quality levels, while the system manages complexity through automated route planning and coordination.
Data Source
AI summary
A computer implemented method includes receiving a map that maps values of a crop characteristic to different locations across a field; receiving harvester route data indicative of a planned route of a harvester at the field; identifying a crop characteristic threshold; identifying a material transfer end location indicative of a location, along the planned route of the harvester, at which a material transfer operation between the harvester and a receiving machine is to end, based on the map, the planned route of the harvester, and the crop characteristic threshold; identifying a material transfer start location range indicative of a geographic area, along the planned route of the harvester, at which the material transfer operation is to start based on the material transfer end location; and generating a route for the receiving machine to travel based on the material transfer end location and the material transfer start location range.