Real-Time Fuel Usage Prediction for Harvesting Operations
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Solution Overview
Problem
Conventional methods for estimating fuel usage in agricultural machines rely on historic field data, which does not account for current field conditions, leading to inaccurate fuel estimation for ongoing operations.
Innovation Solution
The system uses a combination of historic field data and real-time remotely sensed data, including aerial images from satellites or drones, to calculate fuel consumption through machine learning models, updating fuel requirements in real-time based on actual field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods using only historic field data are used for fuel estimation, then the system is simple to operate, but the measurement precision of fuel consumption estimation deteriorates
Solution Approach 1:
The patent combines historic field data with real-time remotely sensed data (aerial images, satellite data) to create a hybrid estimation system. This merging of data sources improves measurement precision by accounting for current field conditions while maintaining operational simplicity through automated data integration and machine learning models.
Solution Approach 2:
The system performs preliminary analysis of aerial images and satellite data to extract current field conditions before combining them with historic data. This preliminary action enables the system to prepare accurate baseline information that enhances fuel consumption estimation precision without increasing operational complexity during actual use.
2Reliability
If only historic field data is used for fuel estimation, then the device complexity is low, but the reliability of fuel estimation deteriorates due to inability to account for current field conditions
Solution Approach 1:
The system incorporates real-time feedback from remotely sensed data (aerial images, satellite imagery) that provides current field conditions such as crop growth stage, soil moisture, and weather impacts. This feedback loop continuously updates the fuel consumption model, improving reliability by ensuring estimates reflect actual current field conditions rather than relying solely on historic averages.
3Measurement precision
If real-time remotely sensed data is integrated with historic data, then the measurement precision of fuel estimation improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary processing of remotely sensed data to extract relevant field condition parameters (crop stage, soil moisture, vegetation indices) before the actual fuel estimation process. This preliminary extraction of key parameters reduces the time required during actual estimation by pre-processing and organizing the remotely sensed data into actionable insights that can be quickly integrated with historic data and machine learning models.
Data Source
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AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for milestone prediction of fuel and chemical usage. An example apparatus includes one or more memories (1914,1916) comprising computer readable instructions (1932); one or more processors (1912) to execute the computer readable instructions (1932) to determine a current amount of fuel required without halt (404) and a current fuel consumption rate for a machine during a harvesting event in a field based on a first amount of fuel required without halt, a first fuel consumption rate, and real time information from sensors (125) of the machine, and determine a real time amount of fuel required based on the current amount of fuel required without halt, the current fuel consumption rate, and a halt time for the machine during the harvesting event, the one or more processors (1912) to use the real time amount of fuel required to schedule fuel delivery for the machine.