Agricultural Map Data Estimation with Error Indication
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Solution Overview
Problem
Conventional agricultural map systems lack techniques for automatically estimating or inferring data parameters accurately and identifying which data can be corrected to improve estimations, leading to potential inaccuracies and a lack of user-friendly decision support for farmers.
Innovation Solution
A novel system and method for generating and displaying maps that utilize estimated information, indicating its usage, and selectively prompting users to supply missing information, with error estimates and authenticity values, while inhibiting further use if confidence checks fail, and utilizing an immutable ledger for data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems require user input for every empty data field, then data accuracy is improved, but user effort and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically estimating and inferring missing data parameters using algorithms that analyze available data patterns, historical information, and relationships between fields. This allows the system to populate empty fields without requiring user input for each field, thereby reducing user effort while maintaining reasonable data accuracy through automated estimation techniques.
Solution Approach 2:
The system performs preliminary actions by pre-populating empty data fields with estimated values before user review. This preliminary estimation process uses available information to generate reasonable defaults, allowing users to review and correct only the most critical or uncertain fields rather than every single field, thus reducing overall time consumption while preserving data accuracy options.
2Ease of operation
If automated estimation techniques are implemented, then user effort is reduced, but data reliability deteriorates due to potential inaccuracies
Solution Approach 1:
The system implements feedback mechanisms by providing users with indicators that show which fields contain estimated values versus verified data. This feedback allows users to identify potentially unreliable data and make targeted corrections. The system also uses feedback loops where user corrections improve future estimation accuracy, thereby maintaining ease of operation while progressively improving data reliability through learning from user interactions.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting estimation confidence levels and visual prominence based on the reliability of estimated versus verified data. Fields with high-confidence estimates are treated similarly to verified data, while low-confidence estimates are more prominently marked for review. This parameter-based differentiation maintains ease of operation by minimizing user effort for high-confidence data while preserving data reliability by encouraging review of low-confidence estimates.
3Measurement precision
If all missing data fields are prominently displayed for user correction, then data accuracy improves, but system complexity and user interface clutter increase
Solution Approach 1:
The system applies local quality by differentiating the visual presentation and interaction requirements of different data fields based on their estimation confidence and importance. Rather than uniformly treating all empty fields the same, the system applies different levels of prominence, marking styles, and correction prompts to specific fields based on local characteristics such as data criticality, estimation reliability, and field relationships. This reduces overall interface complexity while maintaining data accuracy by focusing user attention where it matters most.
Solution Approach 2:
The system implements partial action by selectively prompting users to review and correct only a subset of estimated fields rather than all of them. The system prioritizes fields based on factors such as data criticality, estimation confidence levels, and potential impact on downstream processes. This partial review approach maintains data accuracy for critical fields while reducing user interface complexity and cognitive load by not requiring users to review every single estimated field.
4Ease of operation
If estimated values are used without indication, then map readability improves, but data authenticity deteriorates due to undetected errors
Solution Approach 1:
The system uses color changes and visual differentiation to indicate estimated versus verified data values within the map interface. By applying subtle visual cues such as different coloring, shading, or iconography to fields containing estimated values, the system maintains map readability and overall visual appeal while simultaneously preserving data authenticity information. Users can quickly scan the map to understand its general content while also being able to identify which specific values are estimates versus verified data through these visual indicators.
Data Source
AI summary
A system and method are provided for generating maps based on map types further associated with various data entry fields needed to populate the respective map. A first set of data entry fields are populated for which underlying information is available from at least one data source, and a second set is identified of any data entry fields for which at least some underlying information is missing after the first set is populated. The method further includes selecting estimated information to populate the second set of data entry fields, generating the map including the underlying information and the estimated information in the corresponding data entry fields, and displaying the map on a user interface, wherein the displayed map includes an error estimate indication corresponding to at least one of the second set of data entry fields on at least a portion thereof.