Agricultural Input Performance Ranking System
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Solution Overview
Problem
Agricultural growers face challenges in identifying effective agricultural inputs such as pesticides and fertilizers due to their variable performance across different plant species, locations, and application conditions, with existing information being biased and difficult to interpret from numerous studies.
Innovation Solution
A computer-implemented method using a database to store and analyze historical performance data of agricultural inputs, allowing for the selection and ranking of inputs based on their effectiveness for specific plants and attributes, providing users with informed decisions on input selection and application parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If growers rely on vendor-provided information about agricultural input performance, then they receive product performance assurances, but the information is inherently biased and difficult to interpret
Solution Approach 1:
The patent introduces an independent intermediary system (the database and ranking system) that mediates between vendor claims and grower decisions. This intermediary aggregates data from multiple sources including independent studies, provides objective ranking, and eliminates the bias inherent in vendor-provided information while maintaining reliable performance assessments.
Solution Approach 2:
The system merges information from multiple sources including vendor data, independent research studies, and field performance data into a single comprehensive database. This consolidation allows growers to access balanced, multi-perspective information that overcomes the limitations of single-source vendor information.
2Adaptability or versatility
If growers evaluate agricultural inputs for different plant species and conditions, then they can identify effective inputs, but the variable performance across species, locations, and conditions makes identification difficult
Solution Approach 1:
The system applies local quality by providing tailored recommendations specific to each plant species, growth condition, and target attribute. Instead of generic input recommendations, the database filters and ranks inputs based on local conditions including plant type, disease pressure, environmental factors, and desired outcomes, making the system adaptable to diverse growing scenarios.
Solution Approach 2:
The system segments the complex evaluation process into manageable components: storing data in organized categories (plant species, input types, performance metrics), filtering based on specific criteria, and ranking within discrete categories. This segmentation reduces the complexity of evaluating inputs across multiple variables by breaking down the decision-making process into structured steps.
3Loss of information
If comprehensive historical performance data is collected for multiple agricultural inputs, then informed decisions can be made, but the complexity of interpreting disparate study results increases
Solution Approach 1:
The system transforms disparate study results into standardized parameters including performance rankings, effectiveness scores, and comparative metrics. By converting diverse data sources into uniform parameter formats, the system maintains comprehensive information while reducing interpretation complexity through consistent measurement and evaluation criteria.
Solution Approach 2:
The system incorporates feedback mechanisms where performance data from field applications feeds back into the database, continuously refining and updating input rankings. This feedback loop allows the system to learn from actual performance outcomes and improve the accuracy of recommendations while maintaining manageable complexity through iterative optimization.
Data Source
AI summary
A computer-implemented method for reporting performance of an agricultural input for a plant involves: storing in a computer database a set of agricultural inputs, plants for which the agricultural inputs are used, performance attributes of the agricultural inputs, historical agricultural input performance values, and a relative significance of each of the agricultural inputs for the performance attribute. A computer processor is used to receive a plant selection; receive an attribute to be targeted for the plant selection; identify a plurality of agricultural inputs for affecting the targeted attribute for the plant using the historical agricultural input performance values; rank the plurality of agricultural inputs for affecting the targeted attribute for the selected plant using the relative significance associated therewith; and display the plurality of agricultural inputs and corresponding ranks for the received attribute associated with the selected plant.