Predictive Map Generation for Agricultural Machine Control
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Solution Overview
Problem
Agricultural machines face challenges in optimizing operations due to varying soil properties across fields, such as topography, soil type, and moisture levels, which affect performance and efficiency in harvesting operations.
Innovation Solution
An agricultural work machine generates predictive maps by combining in-situ sensor data with prior soil property maps to predict characteristics like biomass and yield, allowing for real-time adjustments in machine settings and operations to optimize harvesting efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agricultural machines operate with fixed settings across the entire field, then device complexity is reduced and ease of operation is improved, but productivity decreases due to inability to account for varying soil properties
Solution Approach 1:
The system performs preliminary mapping of soil properties (moisture, topography, soil type) across the field before harvesting operations. This advance information allows the control system to pre-determine optimal machine settings for different field zones, enabling proactive adaptation rather than reactive adjustment during harvesting.
Solution Approach 2:
The control system dynamically adjusts machine operating parameters (header height, reel speed, rotor speed, fan speed) based on real-time location and corresponding soil property data from the maps. This creates a dynamically adaptive harvesting system that automatically modifies settings as the machine moves through different soil conditions.
2Measurement precision
If in-situ sensors are used to detect soil characteristics in real-time, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary mapping of soil properties (moisture, topography, soil type) across the field before harvesting operations. This advance information allows the control system to pre-determine optimal machine settings for different field zones, enabling proactive adaptation rather than reactive adjustment during harvesting.
Solution Approach 2:
The system uses an intermediary predictive model that correlates soil properties with crop characteristics (biomass, yield). Instead of directly measuring all crop parameters with energy-intensive sensors, the system uses soil property data as an intermediary to predict crop characteristics, reducing the need for extensive direct measurement while maintaining accuracy.
3Productivity
If machine settings are adjusted frequently to match varying soil conditions, then productivity is improved, but loss of time due to continuous adjustment increases
Solution Approach 1:
The control system dynamically adjusts machine operating parameters (header height, reel speed, rotor speed, fan speed) based on real-time location and corresponding soil property data from the maps. This creates a dynamically adaptive harvesting system that automatically modifies settings as the machine moves through different soil conditions.
Solution Approach 2:
The system incorporates feedback loops where sensor data from the harvesting operation is continuously compared against the predictive maps, and control parameters are automatically adjusted in response. This closed-loop control minimizes manual intervention and optimizes harvesting efficiency in real-time based on actual field conditions.
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.