Predictive Weed Mapping for Automated Harvester Control
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Solution Overview
Problem
Agricultural harvester performance is degraded when encountering weed patches, particularly due to varying weed intensity and types, which can impede machine operation and reduce efficiency.
Innovation Solution
The system generates a predictive weed map using in-situ sensors and prior data to identify weed location, intensity, and type, allowing for automated control adjustments during harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates through fields with weed patches, then the harvesting operation can continue, but the machine performance and efficiency are degraded
Solution Approach 1:
The system performs preliminary detection of weed patches using sensors (optical, capacitive, inductive) before the harvester reaches them. This advance detection allows the control system to prepare and implement speed adjustments or operational modifications in advance, preventing performance degradation rather than reacting after damage occurs.
Solution Approach 2:
The system continuously monitors field conditions using various sensors and feeds this information back to the control system. The control system processes this feedback in real-time and automatically adjusts harvester operations (such as speed or header height) to maintain optimal performance when weed patches are detected, creating a closed-loop control system that maintains productivity while protecting machine performance.
2Reliability
If the operator manually modifies control upon encountering weed patches, then machine performance can be protected, but operational time is lost due to manual intervention
Solution Approach 1:
The harvester system performs self-service by automatically detecting weed patches and adjusting its own operational parameters without human intervention. The control system autonomously processes sensor data and implements control modifications, eliminating the need for the operator to manually respond to weed patches while maintaining machine performance protection.
Solution Approach 2:
The system replaces manual mechanical control with automated electronic control. Instead of the operator physically observing and manually adjusting controls, electronic sensors detect weed patches and an electronic control system automatically adjusts operational parameters, substituting human mechanical intervention with automated electronic systems that operate continuously without time loss.
3Productivity
If automated control systems are implemented to detect and respond to weed patches, then operational efficiency is improved, but device complexity increases
Solution Approach 1:
The automated control system is segmented into distinct functional modules: sensor subsystems (optical, capacitive, inductive sensors for different weed types), data processing subsystem, control decision subsystem, and actuator subsystem. This segmentation allows each component to perform a specific function, making the overall complex system more manageable, maintainable, and scalable while achieving high operational efficiency.
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.