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 resource utilization, as existing data is often generalized and not granular enough to inform precise strategies.
Innovation Solution
A computer-implemented method and system that receives field definition data, retrieves relevant input data from networks, determines field conditions, and provides recommendations for agricultural activities based on these conditions, using a networked agricultural intelligence system that includes user devices, data networks, and an agricultural intelligence computer system to analyze crop-related data and provide field condition data for strategic decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If weather data is generalized for large regions such as counties or states, then data coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the large geographic region into multiple smaller zones, each with its own weather data. Instead of using a single generalized weather dataset for an entire county or state, the system divides the area into distinct zones that can be individually monitored and analyzed, allowing for more precise local weather information while maintaining comprehensive coverage.
Solution Approach 2:
The patent implements local quality by providing customized weather data for specific zones rather than uniform data for the entire region. Each zone receives weather information tailored to its local conditions, enabling growers to make decisions based on hyper-local weather patterns that affect their specific fields rather than generalized regional averages.
2Quantity of substance
If data is aggregated for regional analysis, then data completeness is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system segments aggregated regional data into field-specific datasets. By dividing the comprehensive regional data into smaller, field-level portions, the system maintains the completeness of the overall dataset while providing precise, actionable information for each individual field, enabling both regional analysis and local decision-making.
Solution Approach 2:
The patent extracts specific field-level data from the aggregated regional dataset. By pulling out the relevant portions of data needed for individual fields while maintaining access to the complete regional context, the system provides precise field-level information without losing the broader data completeness needed for comprehensive analysis.
3Reliability
If multiple data sources are integrated for comprehensive analysis, then information reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources into a unified system that provides comprehensive agricultural intelligence. By integrating weather data, soil data, crop data, and market data into a single platform, the system improves information reliability through multiple data validation while managing complexity through integrated data processing and presentation.
Solution Approach 2:
The system implements universality by creating a multi-functional platform that handles diverse data types and provides multiple services. The single system performs weather analysis, soil monitoring, crop management, and market analysis, reducing the need for separate systems while maintaining comprehensive data integration and reliability.
Data Source
AI summary
Systems and methods are provided for managing agricultural activities in a field region. In one example, a computer-implemented method includes identifying temperature grids for the field region, and identifying weather stations for the temperature grids. Each weather station is located at a weather station location in the temperature grids. The computer-implemented method also includes computing weight values based on the weather station locations of the weather stations, such that the weather stations that are more proximate to their respective grids have higher weights than weather stations that are less proximate to their respective grids, and receiving temperature readings from the weather stations. The computer-implemented method then includes computing a plurality of weighted temperatures based on the weight values, computing field condition data for the field region based on the weighted temperatures, and determining at least one field activity for the field region based on the field condition data.