Agricultural Intelligence System for Granular Field Data
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Solution Overview
Problem
Agricultural growers face challenges in making strategic decisions due to difficulties in obtaining reliable and precise data for decision-making, particularly at a granular field level, which affects crop yield and profitability, as existing data is often generalized and not granular enough to inform precise strategies.
Innovation Solution
A computer-implemented method and networked agricultural intelligence system that processes field definition and input data to determine field condition data, providing recommendations on agricultural activities by analyzing weather, soil, and crop conditions at a granular level, using a combination of data networks and AI-driven simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If weather data is generalized for a large region, then data coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the service area into multiple geographic zones (e.g., counties, cities, neighborhoods) and provides weather forecasts for each segment. This allows the system to cover a large area while maintaining precision within each segment by delivering location-specific weather data rather than generalized regional data.
Solution Approach 2:
The patent implements local quality by providing customized weather forecasts tailored to specific locations. Each location receives weather data relevant to its local conditions, ensuring high measurement precision for local weather patterns while the system collectively covers a broad geographic area through multiple localized forecasts.
2Measurement precision
If field condition data is analyzed at a granular level, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that receives raw field condition data from multiple sources, standardizes and validates the data, and transforms it into structured field condition assessments. This intermediary layer simplifies the overall system architecture by handling data processing complexity centrally while enabling precise granular analysis at the field level.
Solution Approach 2:
The patent creates standardized data models and templates for field condition assessment that can be replicated across multiple fields. By using copyable assessment frameworks and standardized data structures, the system achieves granular precision for each field without proportionally increasing complexity, as the same processing logic is reused across different locations.
3Loss of information
If multiple data sources are aggregated, then information completeness is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing data collection protocols, pre-validating data formats, and pre-structuring receiving systems for multiple data sources. This preparation work is done in advance so that when data arrives from multiple sources, the aggregation process is streamlined and occurs more quickly, reducing time loss while maintaining information completeness.
Solution Approach 2:
The patent changes parameters by implementing configurable data aggregation settings that allow adjustment of collection frequency, data source priorities, and processing depth. By making these parameters adjustable, the system can optimize the balance between information completeness and aggregation time based on specific operational needs, delivering comprehensive data when needed without always requiring full aggregation.
Data Source
AI summary
A computer-implemented method for providing variable rate application of crop inputs to an agricultural field includes determining an index value of biomass health for multiple regions in the agricultural field and determining, based on the index value for each of the multiple regions, a number of unique soil types in the agricultural field. The computer-implemented method also includes determining a distinctness of pairs of the unique soil types in the agricultural field, determining a statistical variation based on the distinctness of the pairs, and calculating, based on the statistical variation, a variable rate suitability score for the agricultural field relating to spatially varying a rate of application of one or more crop inputs to the agricultural field. The computer-implemented method then includes displaying the agricultural field along with the variable rate suitability score for the agricultural field.