A Priori Plant Map for Sensor Sampling Window
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Solution Overview
Problem
Existing systems for measuring plant attributes during harvesting, such as row crop harvesters, face noise issues from extraneous vibrations and trash, leading to erroneous interpretations by electronic control units, and require vehicle guidance sensors to establish a sensor sampling window.
Innovation Solution
A system that uses a digital a priori plant location map to anticipate plant presence, allowing the electronic control unit to create a sensor sampling window and improve measurement accuracy by correlating sensor data with plant locations, thereby rejecting noise and optimizing data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle guidance sensors are used to establish the sensor sampling window, then the accuracy of plant attribute measurements is improved, but the device complexity increases
Solution Approach 1:
The system creates a digital a priori plant location map before harvesting operations begin. This map contains pre-determined plant locations that are used to establish the sensor sampling window in advance, eliminating the need for complex vehicle guidance sensors during harvesting. The preliminary action of mapping plant locations resolves the contradiction by providing measurement accuracy through pre-planned sampling windows without adding device complexity during operation.
Solution Approach 2:
The invention creates a digital copy of plant location information through the a priori plant location map. Instead of using physical vehicle guidance sensors to detect and track plants in real-time, the system uses a digital replica of plant positions to define when and where sensor data should be collected. This copying approach maintains measurement precision while significantly reducing device complexity.
2Device complexity
If sensor sampling window is established without a priori plant maps, then the device complexity is reduced, but the measurement precision deteriorates due to noise from extraneous vibrations and trash
Solution Approach 1:
The system performs preliminary mapping of plant locations before harvesting to create the a priori plant location map. This advance preparation enables the establishment of accurate sensor sampling windows without requiring complex real-time vehicle guidance systems, thus maintaining measurement precision while keeping device complexity low.
Solution Approach 2:
The a priori plant location map serves as an intermediary between the simple sensor system and the plant attributes being measured. Instead of directly using complex vehicle guidance sensors to improve precision, the system uses this digital map as a mediator to establish sampling windows, achieving accurate measurements with simpler hardware.
3Quantity of substance
If continuous sensor data collection is performed, then the quantity of data is increased, but the loss of time and processing efficiency increase due to noise filtering requirements
Solution Approach 1:
The system uses periodic sampling based on the a priori plant location map to determine when to collect sensor data. Instead of continuous data collection, the sensor activates only during predetermined time windows when plants are expected to pass by. This periodic action reduces the total quantity of data collected while maintaining measurement quality, thereby reducing processing time and eliminating unnecessary noise filtering.
Solution Approach 2:
The plant locations are predetermined and stored in the a priori map before harvesting begins. This preliminary knowledge allows the system to plan sampling windows in advance, collecting data only when needed. This approach optimizes the quantity of useful data collected while minimizing processing time by avoiding continuous data acquisition and extensive noise filtering.
Data Source
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AI summary
A system for measuring plant attributes comprises a plant attribute sensor (103, 108, 110); a digital a priori plant location map (118); and an ECU (126) coupled to the plant attribute sensor (103, 108, 110) and configured to retrieve and use the a priori plant map and to anticipate a plant measurement based upon the a priori plant map and to use that anticipation to improve the accuracy of the plant measurement.