Predictive Map Generation for Agricultural Harvester Control
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Solution Overview
Problem
Agricultural harvester performance is hindered by topographic features such as slopes, leading to increased power demands, grain loss, and reduced grain quality due to pitch and roll, especially in wet conditions, where slippage and instability issues arise.
Innovation Solution
The generation of predictive maps using in-situ sensor data and prior topographic maps to model relationships between topographic characteristics and machine performance metrics, enabling automated control adjustments for improved power utilization, material distribution, and grain quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates on sloped terrain, then the harvesting operation can continue across the field, but power demands increase and machine performance deteriorates due to pitch and roll
Solution Approach 1:
The system performs preliminary action by obtaining topographic map data before the harvesting operation and using it to predict pitch and roll conditions. This allows the control system to proactively adjust machine settings and power allocation before the harvester encounters difficult terrain, rather than reacting after performance degradation occurs
Solution Approach 2:
The system implements dynamic adjustment of machine control parameters based on real-time location data and predicted topographic conditions. The control system continuously adapts power distribution, harvesting settings, and machine operation to match the specific terrain conditions at each location, optimizing performance across varying slopes
2Area of stationary object
If the harvester travels over sloped features, then the harvesting coverage is maintained, but grain loss increases due to pitch and roll effects
Solution Approach 1:
The system uses pre-obtained topographic maps to predict pitch and roll conditions before the harvester reaches sloped areas. This preliminary information allows the control system to adjust harvesting parameters such as header height, reel speed, and concave clearance in advance, preventing grain loss caused by machine instability on slopes
Solution Approach 2:
The system implements feedback control by continuously monitoring the harvester's location via GPS, comparing it with the topographic map data, and automatically adjusting harvesting parameters to compensate for predicted pitch and roll effects, thereby maintaining harvesting efficiency and reducing grain loss on difficult terrain
3Reliability
If automated control adjustments are implemented to mitigate topographic effects, then machine performance is optimized, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single integrated control platform that handles multiple tasks: receiving and processing topographic map data, tracking harvester location via GPS, predicting pitch and roll conditions, and adjusting various harvesting parameters. This universal control system consolidates what would otherwise require multiple separate systems, reducing overall complexity while maintaining comprehensive performance optimization
Solution Approach 2:
The system introduces a control system as an intermediary layer between the topographic data and the harvester's mechanical components. This intermediary processes terrain information and translates it into appropriate control signals for various machine subsystems, simplifying the complexity by providing a centralized intelligence layer rather than requiring direct complex interactions between all components
Data Source
AI summary
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.