Predictive Crop Maps for Adaptive Harvester Setting Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural harvesters face challenges in adapting to varying field conditions, such as crop density and previous agricultural operations, which affect harvesting efficiency and machine performance, as existing systems lack effective predictive control mechanisms.
Innovation Solution
The use of in-situ sensors and prior operation data to generate predictive maps that anticipate agricultural characteristics, allowing for automated control adjustments during harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated control mechanisms are implemented, then harvesting efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary mapping of field conditions (crop density, moisture, previous operations) before harvesting begins. This advance preparation allows the automated control system to make informed decisions without requiring complex real-time analysis, thereby improving harvesting efficiency while managing system complexity through pre-computed data layers.
Solution Approach 2:
The patent introduces an automated control system as an intermediary between the harvester and the field conditions. This intermediary processes information from multiple sources (maps, sensors) and translates it into control decisions, simplifying the overall system architecture while enabling efficient automated harvesting operations.
2Adaptability or versatility
If real-time sensor data is collected and processed, then adaptability to field conditions is improved, but information processing requirements increase
Solution Approach 1:
The system pre-processes and stores field condition data (crop density, moisture content, previous agricultural operations) in detailed maps before harvesting. This preliminary information preparation reduces the real-time processing burden during harvesting while maintaining high adaptability, as the control system can query pre-computed data rather than analyzing raw sensor data in real-time.
Solution Approach 2:
The patent creates simplified representations (maps) of complex field conditions that can be easily processed and stored. These maps serve as copies of the actual field state, allowing the control system to make adaptive decisions based on processed information rather than raw data, thereby reducing information processing requirements while maintaining adaptability.
3Manufacturing precision
If predictive maps are generated using historical and real-time data, then control precision is improved, but computational requirements increase
Solution Approach 1:
The system generates predictive maps by combining historical field data with current sensor readings before the harvesting decision is needed. This preliminary computation of predictive information allows for high control precision during harvesting without requiring intensive real-time calculations, as the predictive models are prepared in advance and can be queried efficiently during operation.
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.