Predictive Weed Mapping for Harvester Speed and Header Adjustment
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Solution Overview
Problem
Agricultural harvesting machines face performance degradation when encountering weed patches, as weeds can impede machine operation and vary in intensity, type, and moisture content, making it challenging to predict and control harvester performance effectively.
Innovation Solution
The system generates a predictive weed map using in-situ sensors and prior data, such as vegetative index maps, to identify weed location, intensity, and type, allowing for automated control of the harvester to optimize performance by adjusting settings like header height and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates at standard speed and settings, then harvesting productivity is maintained, but performance degrades when encountering weed patches
Solution Approach 1:
The system performs preliminary detection of weed patches using sensors (optical, capacitive, or other types) mounted on the harvester. The control system receives sensor data indicating weed presence ahead of the harvesting mechanism and proactively adjusts operating parameters (speed, header height, rotor speed) before the harvester encounters the weed patch, preventing performance degradation rather than reacting after it occurs.
Solution Approach 2:
The control system dynamically adjusts harvester operating parameters in real-time based on detected weed conditions. The system continuously monitors sensor data and modifies speed, header height, and other parameters adaptively as the harvester moves through different field zones with varying weed intensities, allowing optimal performance across heterogeneous field conditions.
2Reliability
If the harvester adjusts operating parameters to navigate weed patches, then performance reliability is maintained, but productivity decreases due to speed reductions and operational modifications
Solution Approach 1:
The control system applies localized adjustments only to specific harvester components when weed patches are detected, rather than reducing overall operating speed. For example, it may adjust header height or rotor speed locally while maintaining forward travel speed, or modify cleaning fan speed only in affected zones. This targeted approach maintains productivity while ensuring reliable operation through weed patches.
3Measurement precision
If the system uses multiple sensor types and real-time data processing, then measurement precision of weed characteristics is improved, but device complexity increases
Solution Approach 1:
The control system acts as an intermediary that receives data from multiple sensor types (optical sensors, capacitive sensors, other detection devices) and processes this information to generate unified control signals. The control system integrates inputs from various sensors and translates them into coordinated adjustments of harvester parameters, managing the complexity of multiple sensors through centralized processing and coordination.
Data Source
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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.