Fuel Usage Milestone Prediction With Real-Time Field Sensing
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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 predictions.
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, and updates 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 prediction deteriorates
Solution Approach 1:
The patent combines historic field data with real-time remotely sensed data (aerial images from satellites or drones) to create a hybrid estimation system. This merging of data sources improves measurement precision by accounting for current field conditions while maintaining reasonable system complexity through integrated processing.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that process and integrate both historic and real-time data. These models act as mediators between raw data from multiple sources and the final fuel consumption prediction, enabling accurate predictions without requiring direct complex integration of all data sources.
2Measurement precision
If real-time remotely sensed data and machine learning models are used, then the measurement precision of fuel estimation improves, but the device complexity increases
Solution Approach 1:
The patent performs preliminary actions by collecting and processing remotely sensed data before fuel consumption calculations are needed. Aerial images are captured and analyzed in advance to determine current field conditions, allowing the system to prepare accurate predictions without requiring complex real-time processing during operation.
Solution Approach 2:
The system uses autonomously collected remotely sensed data that serves itself by providing current field condition information without requiring external manual input. The machine learning models automatically process this self-collected data to generate fuel consumption predictions, reducing the need for external intervention and simplifying operation despite the sophisticated underlying technology.
3Loss of time
If accurate real-time fuel estimation is implemented, then the loss of time due to fuel shortages is reduced, but the use of energy for data processing increases
Solution Approach 1:
The system performs preliminary fuel consumption calculations using collected remotely sensed data before the agricultural machine begins operation or during idle periods. By predicting fuel consumption in advance and identifying potential shortages beforehand, the system prevents downtime without requiring continuous high-energy real-time processing during critical operations.
Solution Approach 2:
The patent implements feedback mechanisms where fuel consumption predictions are continuously updated based on actual field conditions and historical performance data. This feedback loop allows the system to refine its estimates over time, improving accuracy and reducing unnecessary energy consumption by avoiding redundant calculations when predictions are already reliable.
Data Source
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 comprising computer readable instructions; one or more processors to execute the computer readable instructions to determine a current amount of fuel required without halt 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 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 to use the real time amount of fuel required to schedule fuel delivery for the machine.