Windrower Predictive Weed Mapping for Sensor-Limited Fields
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Solution Overview
Problem
Existing mobile agricultural windrowing machines struggle to effectively detect weeds due to sensor limitations such as latency, visibility issues from dust/debris, and low light conditions, which affects the ability to adjust operations in real-time.
Innovation Solution
A system that generates predictive weed maps using in-situ sensors and historical or prior data to provide proactive control of windrowing machines by integrating predictive models with information maps like historical performance, vegetative index, crop genotype, soil type, soil moisture, soil nutrient, and optical maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-situ sensors are used to detect weeds in real-time, then weed detection capability is improved, but sensor reliability deteriorates due to latency, dust/debris visibility issues, and low light conditions
Solution Approach 1:
The system performs preliminary actions by generating predictive weed maps before the windrowing operation using historical data and environmental factors. This allows the control system to proactively adjust machine operations in advance rather than reacting to real-time sensor data that may be unreliable due to dust, debris, and lighting conditions.
Solution Approach 2:
The patent introduces an intermediary predictive modeling system that bridges the gap between unreliable real-time sensor data and operational control decisions. The predictive weed maps serve as an intermediary representation of weed locations, derived from multiple data sources including historical performance data, vegetative index maps, and environmental conditions, rather than relying solely on direct sensor detection.
2Speed
If real-time sensor detection is used for weed identification, then operational responsiveness is improved, but measurement precision deteriorates due to environmental conditions
Solution Approach 1:
The system generates predictive weed maps in advance of the windrowing operation, allowing operational adjustments to be made proactively rather than reactively. This preliminary action ensures both responsiveness (by having predictions ready) and precision (by using multiple data sources rather than relying on compromised real-time sensor data).
Solution Approach 2:
The patent merges multiple data sources including historical performance data, vegetative index maps, soil type maps, and environmental conditions to create a comprehensive predictive weed map. This combination compensates for the weaknesses of any single detection method and provides more reliable and precise weed location information.
3Productivity
If proactive weed detection using predictive models is implemented, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The control system is designed to be multi-functional, handling not only predictive weed map generation but also historical data management, real-time sensor data processing, machine operation control, and predictive modeling. By consolidating these functions into a single universal control system, the patent manages complexity while achieving proactive operational efficiency.
Solution Approach 2:
The system performs self-service by automatically generating predictive weed maps and using them to control windrowing operations without requiring constant human intervention. The control system autonomously processes data, generates predictions, and adjusts machine operations, reducing the need for manual monitoring and decision-making while improving operational efficiency.
Data Source
AI summary
One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a weed value as a mobile machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive weed values at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the weed value detected by the in-situ sensor. The predictive map can be output and used in automated machine control.