Predictive Material Consumption Mapping for Variable-Rate Field Application
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Solution Overview
Problem
Current agricultural systems face challenges in efficiently delivering materials like fertilizers and pesticides, as they often rely on blanket applications that do not account for varying field conditions, leading to suboptimal use and potential environmental impact.
Innovation Solution
An agricultural system that generates an information map of field characteristics and uses in-situ sensors to detect material consumption values. This data is used to create predictive maps that guide the controlled application of materials, ensuring optimal distribution based on real-time field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If blanket material application is used across the field, then material delivery is simplified and operation speed is maintained, but material consumption efficiency deteriorates and environmental harm increases
Solution Approach 1:
The system applies material at different rates to different geographic locations within the field based on locally varying conditions. The predictive material consumption map divides the field into zones with specific material needs, allowing precise local adjustment of application rates rather than uniform blanket application across the entire field.
Solution Approach 2:
The system generates predictive material consumption maps before material application occurs, using historical data, weather forecasts, and crop models to anticipate material needs. This preliminary mapping allows the system to proactively adjust application rates in advance, preventing both over-application and under-application of materials.
2Ease of operation
If uniform material application is applied across all field areas, then device complexity is reduced and operation is simplified, but manufacturing precision and application accuracy deteriorate
Solution Approach 1:
The system dynamically adjusts material application rates in real-time based on continuously updated predictive maps and current field conditions. The application precision is maintained through automated control systems that modify delivery parameters on-the-fly, allowing high precision without requiring complex manual intervention or pre-planning for each zone.
3Measurement precision
If real-time sensor detection and predictive mapping are implemented, then material consumption precision is improved and waste is reduced, but device complexity and system cost increase
Solution Approach 1:
The system uses multi-functional sensors and data processing capabilities that serve multiple purposes. The same sensor network and computational platform that generate predictive material consumption maps also monitor crop health, soil conditions, and weather parameters, reducing the need for separate specialized systems and thereby limiting the increase in overall device complexity.
4Loss of energy
If variable rate material application is implemented based on predictive maps, then material use efficiency is improved and environmental impact is reduced, but operation time and processing complexity increase
Solution Approach 1:
The system maintains continuous operation by generating predictive maps in real-time during the application process rather than requiring separate pre-mapping and application phases. The material application proceeds continuously with dynamic rate adjustments, eliminating downtime for map generation and maintaining operational momentum throughout the field treatment.
Data Source
AI summary
An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects material consumption values as a mobile material application machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive material consumption values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and material consumption values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.