Turfgrass Performance Prediction via Regional Attribute Segmentation
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Solution Overview
Problem
Predicting turfgrass performance across different geographic regions is challenging due to varying evaluation data and the large amount of information associated with each turfgrass variety, making it difficult for growers and consumers to select suitable seed or sod that meets their needs.
Innovation Solution
A computer-implemented method using a database to store and analyze historical grass attribute values for various turfgrass seed varieties, allowing users to select target regions and calculate weighted averages of common attributes to predict performance in specific geographic areas, displayed in a graphical format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive turfgrass evaluation data is collected for multiple attributes and regions, then the accuracy of performance prediction is improved, but the complexity of information processing and analysis increases
Solution Approach 1:
The patent segments the comprehensive evaluation data into standardized attribute categories (color, density, moisture requirements, disease resistance, etc.) and organizes them by geographic region. This segmentation allows the system to process large amounts of information systematically by breaking it down into manageable, standardized units that can be analyzed independently and then synthesized for prediction.
Solution Approach 2:
The patent introduces a computer processor as an intermediary that automatically processes, analyzes, and synthesizes the comprehensive evaluation data. The processor acts as a mediator between the raw data and the final prediction output, performing tasks such as data cleaning, attribute standardization, regional comparison, and performance prediction without requiring manual analysis of the complex information.
2Quantity of substance
If detailed turfgrass attribute information is provided for each variety, then the completeness of information is improved, but the ease of selecting suitable turfgrass for specific regions deteriorates
Solution Approach 1:
The patent extracts and highlights only the most relevant turfgrass attributes for each geographic region based on local conditions. Instead of presenting all available data uniformly, the system identifies and emphasizes attributes that are most important for prediction in specific regions (e.g., drought tolerance for arid regions, cold hardiness for northern regions), thereby simplifying the selection process while maintaining information completeness.
Solution Approach 2:
The patent applies local quality by tailoring the presentation and analysis of turfgrass information to specific geographic regions. Each region receives customized information highlighting attributes most relevant to its climate and soil conditions. The system adjusts the weight and emphasis of different attributes based on regional characteristics, making the information more actionable and easier to interpret for local selection decisions.
3Duration of action of moving object
If turfgrass evaluation is performed annually for all varieties, then the currency of data is improved, but the cost and time required for evaluation increases
Solution Approach 1:
The patent performs preliminary actions by conducting comprehensive turfgrass evaluations in advance during controlled nursery conditions before the turfgrass is deployed in target regions. The system establishes baseline performance data and regional adaptation characteristics beforehand, allowing this information to be stored and retrieved for future prediction without requiring repeated field evaluations for each new planting decision.
Solution Approach 2:
The patent creates a digital copy of comprehensive evaluation data stored in a database that can be queried and analyzed repeatedly without additional physical evaluation time. The system maintains a repository of historical and current evaluation data that can be accessed instantly for prediction purposes, eliminating the need to re-evaluate turfgrass varieties for each new selection decision while keeping data current through periodic updates.
Data Source
AI summary
Grass seed performance may be predicted by receiving grass seed selections for individual grass seed varieties and target geographic regions for growing the grass seed selection. A common set of grass attributes is identified from historical grass attribute values associated with the selected target regions. The historical grass attribute values in the first common set of grass attributes are retrieved and are displayed in a graphical format for the at least two individual grass seed varieties in the at least two selected target regions. A weighted average may be calculated for the historical grass attribute values in the common set and may provide a prediction of performance of the individual grass seed varieties across the selected geographic regions or in regions proximate the selected geographic regions.