Agricultural Telematics System for Real-Time Geospatial Data Classification
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Solution Overview
Problem
Current farm management systems face inefficiencies in processing and analyzing large amounts of geospatial data from agricultural equipment, requiring manual and computationally expensive processes to generate actionable insights, which can lead to uninformed decisions due to incomplete data analysis, especially regarding equipment usage and travel data.
Innovation Solution
A system that passively collects and transfers agricultural geospatial data for adaptive classification into operational, travel, and ancillary categories, using an adaptive data analysis algorithm to generate actionable management information and insights in real-time, dynamically matching data events to an agricultural operations model, and providing operational directives to improve decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and analysis processes are used, then data accuracy can be maintained, but processing time and computational cost increase significantly
Solution Approach 1:
The system performs preliminary classification of geospatial data events into operational, travel, and ancillary categories as data is being collected and transmitted. This preliminary action allows the data to be organized and structured before reaching the analysis stage, reducing the time required for subsequent processing while maintaining accuracy through systematic categorization.
Solution Approach 2:
The patent introduces an intermediary data processing layer that receives raw geospatial data, classifies it into meaningful categories, and transforms it into structured information before passing it to analysis tools. This intermediary step acts as a bridge between raw data collection and final analysis, enabling automated processing without sacrificing data accuracy.
2Loss of information
If comprehensive geospatial data is collected from multiple sources, then management decision quality improves, but data processing complexity and cost increase
Solution Approach 1:
The system segments comprehensive geospatial data into distinct categories: operational events (when equipment is working), travel events (when equipment is moving between locations), and ancillary events (other activities). This segmentation breaks down complex multi-source data into manageable, categorized units that can be processed independently, reducing overall processing complexity while preserving information completeness.
Solution Approach 2:
The patent applies different processing approaches to different data categories based on their specific characteristics. Operational data receives one type of analysis, travel data receives another, and ancillary data receives yet another. This local quality approach optimizes processing for each data type while maintaining comprehensive information coverage.
3Speed
If real-time data analysis is implemented, then actionable insights are provided timely, but computational resources and processing power increase
Solution Approach 1:
The system performs partial analysis in real-time by classifying data events into categories as they occur, providing immediate actionable insights about equipment status and location. More comprehensive analysis, including detailed cost calculations and performance metrics, is performed on subsets of categorized data rather than the entire dataset, reducing computational energy requirements while maintaining timely response capability.
Data Source
AI summary
A machine control system includes an agricultural work machine having an ECU coupled via a system bus to control engine functions, a GPS receiver, data collector, and specialized guidance system including a stored program. The data collector captures agricultural geospatial data including location data for the work machine and data from the ECU, and executes the stored program to: (a) capture geometries of the farm; (b) capture agricultural geospatial data; (c) automatically classify the agricultural geospatial data using the geometries of the farm, into activity/event categories including operational, travel, and ancillary events; (d) aggregate the classified data to create geospatial data events; (e) match the geospatial data events to a model to generate matched events; (f) use the matched events to generate actionable information for the working machine in real time or near real-time; and (g) send operational directives to the agricultural work machine based on the actionable information.