Predictive Field Mapping for Automated Harvester Control
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Solution Overview
Problem
Agricultural machines, such as harvesters, face performance degradation due to varying topography and biomass conditions, leading to issues like grain loss, quality, and internal material distribution, which existing systems struggle to address effectively in real-time.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate maps that predict agricultural characteristics, allowing for automated machine control and optimizing operations based on topographic, biomass, and vegetative index data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensing and predictive mapping are implemented, then machine performance and grain quality are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary mapping of agricultural characteristics across the field before harvesting operations begin. This predictive map is created in advance using sensor data and machine learning models, allowing the harvester to proactively adjust settings rather than reactively responding to conditions during harvesting, thereby improving performance without requiring complex real-time sensing systems
Solution Approach 2:
The system creates a digital copy or predictive map of the field's agricultural characteristics based on sensor data and machine learning. This virtual representation allows the control system to simulate and optimize harvesting parameters without requiring direct real-time measurement of every parameter during operation, reducing device complexity while maintaining performance
2Loss of substance
If real-time adjustments are made based on predictive mapping, then grain loss is reduced, but measurement precision requirements increase
Solution Approach 1:
The system introduces a predictive map as an intermediary between sensor measurements and control decisions. Rather than requiring direct real-time measurement of grain loss conditions, the predictive map translates sensor data into actionable insights about upcoming field conditions, allowing the system to preemptively adjust settings to minimize grain loss without demanding ultra-precise real-time measurements
Solution Approach 2:
The system performs preliminary analysis of sensor data to create predictive maps of agricultural characteristics before harvesting reaches those areas. This allows the system to prepare optimal settings in advance based on predicted conditions rather than attempting to measure and respond to actual conditions in real-time, reducing measurement precision requirements while still preventing grain loss
3Productivity
If automated control is implemented using predictive maps, then productivity increases, but device complexity increases
Solution Approach 1:
The system enables the harvester to automatically adjust its own operating parameters based on the predictive map and sensor feedback. The machine self-regulates settings such as header height, reel speed, and concave clearance without requiring complex external control systems or constant operator intervention, thereby improving productivity while keeping the control architecture relatively simple
Solution Approach 2:
The predictive map provides advance information about field conditions, allowing the automated control system to pre-plan and execute optimal harvesting strategies. This preliminary guidance enables simpler control logic to achieve higher productivity by making informed decisions based on predicted conditions rather than requiring complex real-time optimization algorithms
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.