Predictive Speed Map for Agricultural Harvester Control
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Solution Overview
Problem
Agricultural harvester performance is affected by various field conditions such as biomass, crop state, topography, soil properties, and seeding characteristics, leading to inefficiencies in harvesting operations due to the need for manual adjustments in speed and machine settings.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate predictive maps that adjust machine speed and settings in real-time based on sensed conditions, ensuring a constant feed rate and optimal performance across different field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustments in speed and machine settings are made during harvesting, then operator control and adaptability are maintained, but harvesting efficiency and productivity are reduced due to the time and effort required for adjustments
Solution Approach 1:
The system enables the harvester to automatically adjust its own speed and settings by using sensors to detect field conditions and a control system to modify operational parameters without operator intervention, making the machine self-regulating based on real-time conditions
Solution Approach 2:
The system continuously monitors field conditions through sensors and feeds this information back to the control system, which then adjusts machine parameters accordingly, creating a closed-loop control system that automatically adapts to changing conditions
2Stability of the object's composition
If machine speed is reduced to maintain constant feed rate under varying field conditions, then feed rate consistency is improved, but overall harvesting productivity decreases
Solution Approach 1:
The system dynamically adjusts machine speed based on real-time field conditions detected by sensors, allowing the harvester to optimize between maintaining constant feed rate and maximizing harvesting speed by automatically modifying operational parameters throughout the field
Solution Approach 2:
The system applies different speed and operational settings to different locations within the field based on local conditions detected by sensors, allowing each zone to be harvested at the optimal speed for maintaining feed rate consistency while maximizing overall productivity
3Productivity
If automated control systems are implemented to adjust machine parameters, then harvesting efficiency and productivity are improved, but device complexity increases
Solution Approach 1:
The control system is designed to perform multiple functions including sensor data acquisition, condition analysis, parameter optimization, and automated adjustment of various machine components, consolidating complex control tasks into a single multi-functional system that improves efficiency without proportionally increasing complexity
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.