Cloud Calibration Models for Adaptive Vehicle Spectral Analysis
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Solution Overview
Problem
Existing agricultural sensor calibration models are often outdated and inaccurate due to the high costs and time required for laboratory testing, and preloaded models on vehicles do not account for varying crop types and conditions, leading to inefficiencies in generating accurate spectral data analysis.
Innovation Solution
A cloud-based system that utilizes interconnected vehicles to generate and update calibration models using crowdsourced data from multiple locations, enabling continuous re-training of neural network models based on reference data and spectra, reducing the need for extensive laboratory testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration models are generated using traditional laboratory testing methods, then measurement precision is improved, but loss of time and loss of substance increase significantly
Solution Approach 1:
The patent combines multiple vehicles' spectral data and reference data into a centralized cloud-based system, merging previously isolated calibration processes into a unified model generation approach that leverages collective data for improved efficiency and accuracy
Solution Approach 2:
The calibration model generated through this system serves multiple vehicles and multiple crop types simultaneously, creating a universal model that replaces the need for separate laboratory testing for each individual vehicle or crop type
2Adaptability or versatility
If preloaded calibration models are used on vehicles, then device complexity is reduced, but adaptability deteriorates as models cannot account for varying crop types and conditions
Solution Approach 1:
The patent introduces a cloud-based server as an intermediary between vehicles and calibration models, allowing vehicles to access and update models without complex local processing capabilities, thus maintaining simple onboard devices while achieving high adaptability
Solution Approach 2:
The calibration model transitions from a static preloaded state to a dynamic, continuously updating model that adapts to varying crop types and conditions through ongoing data collection and retraining cycles
3Manufacturing precision
If extensive laboratory testing is performed to generate accurate calibration models, then manufacturing precision is improved, but productivity decreases due to high costs and time requirements
Solution Approach 1:
The system implements continuous data collection and model retraining operations, where vehicles continuously gather spectral data and the system continuously retrains models, eliminating the discontinuous, batch-oriented laboratory testing process
Solution Approach 2:
The system enables self-service calibration model generation by automatically collecting reference data from vehicles, processing spectral information, and generating updated models without requiring manual laboratory intervention for each calibration cycle
Data Source
AI summary
Methods and apparatus to generate calibration models in a cloud environment are disclosed. An example apparatus includes training circuitry to generate a calibration model based on a correlation of reference data and spectra, the reference data based on physical samples collected by one or more vehicles, the spectra associated with the physical samples, and distribution circuitry to provide, via a network communication, the calibration model to the one or more vehicles.