Crop Loss Sensor Correction Using Harvesting Machine Context
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Solution Overview
Problem
Current crop loss sensing systems in harvesting machines often inaccurately measure crop loss due to various contextual factors such as machine state, crop properties, and environmental conditions, leading to inefficiencies and potential losses.
Innovation Solution
A crop loss correction system that receives sensor signals from various loss sensors and uses context information from a set of context sensing components to correct these signals, providing a more accurate measurement of crop loss through a knowledge base and correction component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional crop loss sensors are used to measure crop loss during harvesting, then crop loss can be detected, but the measurement precision is poor due to contextual factors such as machine state, crop properties, and environmental conditions
Solution Approach 1:
The sensing system is divided into multiple independent sensor components, each measuring specific contextual parameters (machine state, crop properties, environmental conditions). This segmentation allows each sensor to focus on a specific aspect, improving overall measurement precision while maintaining manageable system complexity through modular design
Solution Approach 2:
The system integrates multiple sensors that serve universal purposes - each sensor collects data that can be used for different correction objectives. The same sensor data is used to correct for various contextual factors (machine speed, crop moisture, environmental conditions), making the sensing system multi-functional and reducing the need for separate specialized sensors
2Measurement precision
If multiple context sensing components are added to correct sensor signals, then measurement precision improves, but device complexity increases
Solution Approach 1:
Multiple context sensing components are merged into a unified correction system that processes all sensor inputs through a single correction algorithm. This combining approach consolidates the complexity into one integrated system rather than requiring separate correction mechanisms for each sensor, improving precision while managing overall system complexity
Solution Approach 2:
The system implements feedback by continuously monitoring contextual parameters and using this information to dynamically correct the crop loss sensor signals. The correction component receives ongoing input from multiple sensors and adjusts the loss measurement in real-time, improving accuracy while using a standardized feedback loop structure that prevents complexity escalation
Data Source
AI summary
A crop loss correction system receives one or more crop loss sensor signals that are indicative of crop lost by a harvesting machine. A correction component receives context information from a set of context sensing components to identify a context of the harvesting machine. The correction component corrects the crop loss sensor signals, based upon the context information, to obtain a corrected loss signal indicative of the sensed crop loss, corrected based on the mobile machine context. The corrected loss signal is output to an output device.