Augmented Agricultural Data Display for Real-Time Field Decisions
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Solution Overview
Problem
Current methods for farmers to gather and correlate agricultural data are cumbersome, time-consuming, error-prone, and consume significant processing and memory resources, leading to imprecise decision-making.
Innovation Solution
A system that preprocesses and stores georeferenced agricultural data, displaying it in augmented reality to enhance objects with computer-generated perceptual information, using cloud computing to access and analyze data from various sources, and providing diagnostic and prognostic workflows to assist farmers in decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to gather and correlate agricultural data, then farmers can access basic information, but the process is cumbersome, time-consuming, and error-prone
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing georeferenced agricultural data from multiple sources before farmers need to make decisions. Data is collected, validated, and organized in advance, allowing farmers to access ready-to-use information without performing manual data gathering tasks during critical decision-making periods.
Solution Approach 2:
The system introduces an intermediary layer between raw agricultural data sources and farmer decision-making. This intermediary system automatically correlates and contextualizes data from diverse sources (weather, soil, satellite imagery, historical records), transforming raw data into actionable insights and eliminating the need for farmers to manually process and cross-reference multiple data sources.
2Measurement precision
If traditional methods are used to gather and correlate agricultural data, then basic information can be accessed, but the process is error-prone and consumes significant processing resources
Solution Approach 1:
The system performs self-service by automatically executing data collection, validation, correlation, and contextualization processes without requiring manual intervention. The system self-manages the complex processing of agricultural data, reducing human error and minimizing the processing overhead that would otherwise be required for manual data correlation and verification.
Solution Approach 2:
The system replaces manual mechanical processes of data gathering and correlation with automated digital systems. Instead of farmers manually collecting and cross-referencing data from multiple sources, the system uses automated algorithms and computational processes to correlate data from diverse sources, improving accuracy while reducing processing complexity.
3Reliability
If manual data gathering methods are used, then farmers can access information, but decision-making becomes imprecise
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor and refine data correlation processes. By analyzing the quality and relevance of correlated data, the system adjusts its processing algorithms to improve accuracy over time, ensuring that the information provided to farmers is increasingly precise and reliable for better decision-making.
Solution Approach 2:
The system changes key parameters of data processing by transitioning from manual to automated processes. This includes changing the scale of data processing (from individual farmer level to system-wide automation), the precision of data correlation (from basic to multi-parameter analysis), and the timeliness of information delivery (from manual retrieval to real-time access).
Data Source
AI summary
A geographic position of an agricultural machine is captured. Agricultural data is received that corresponds to a geographic position. Georeferenced visual indicia are displayed that are indicative of the received agricultural data.