Hybrid Placement Visualization Using Field Profile Probability Fit
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Solution Overview
Problem
Conventional techniques for determining optimal hybrid or variety placement in agricultural fields lack repeatable methods for quantifying field productivity and product performance, leading to flawed recommendations due to human intuition and inadequate data processing, and fail to address intra-and inter-field variability, resulting in divergent outcomes.
Innovation Solution
A computing system utilizing unsupervised clustering algorithms to analyze environmental data, generate field profiles, and compute the probability of fit for hybrid/variety placement by integrating probability density functions, enabling accurate and repeatable decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques rely on human intuition for hybrid/variety selection, then decision-making is simple and quick, but the recommendations are flawed and lack repeatability
Solution Approach 1:
The patent replaces human intuition and manual decision-making with an automated computing system that processes environmental data through unsupervised clustering algorithms. This substitution eliminates the unreliability of human judgment while maintaining operational simplicity through automated recommendations.
Solution Approach 2:
The system performs self-service by automatically analyzing environmental data, generating field profiles, matching hybrids/varieties, and producing recommendations without requiring human expertise in agricultural science. The computing system serves itself to generate reliable, repeatable recommendations.
2Loss of information
If conventional approaches use large volumes of agricultural data, then more information is available for decision-making, but the data cannot be processed or visualized effectively
Solution Approach 1:
The patent extracts meaningful patterns from large volumes of environmental data by applying unsupervised clustering algorithms. The system isolates key field characteristics and generates condensed field profiles that capture essential information without requiring processing of the entire raw dataset, thus maintaining both information quality and decision-making speed.
Solution Approach 2:
The system transforms raw environmental data into standardized field profiles through parameter transformation. By changing the representation of data from raw measurements to clustered profile characteristics, the system enables efficient comparison and matching while preserving all relevant information.
3Measurement precision
If conventional techniques use unrealistic assumptions about field productivity, then the analysis is simplified, but the recommendations become unrealistic and lead to guesswork
Solution Approach 1:
The system incorporates feedback by using actual environmental data from the specific field being analyzed, rather than relying on generic assumptions. The unsupervised clustering algorithm learns from the actual field characteristics and uses this feedback to generate accurate, site-specific recommendations that reflect real conditions.
Solution Approach 2:
The patent changes the parameters used for analysis from unrealistic conventional assumptions to actual measured environmental parameters. By transforming the input data to reflect real field conditions including soil properties, climate data, and historical performance, the system achieves precise yield predictions without excessive complexity.
4Adaptability or versatility
If conventional approaches apply a one-size-fits-all method to hybrid/variety placement, then the process is simple and uniform, but intra- and inter-field variability are ignored leading to divergent outcomes
Solution Approach 1:
The patent segments the field into distinct profiles based on environmental characteristics using unsupervised clustering. This segmentation allows the system to identify and accommodate intra-field variability by matching different hybrid/variety options to specific field segments, while the automated process maintains operational simplicity.
Solution Approach 2:
The system applies local quality by generating specific recommendations tailored to each field profile rather than applying a uniform approach across the entire field. Each segment receives customized hybrid/variety recommendations based on its unique environmental characteristics, improving adaptability while the automated system maintains ease of operation.
Data Source
AI summary
A computing system for providing product performance visualizations includes a processor; and a memory having stored thereon instructions that, when executed by the one or more processors, cause the computing system to: receive environmental data; analyze the environmental data; select data corresponding to a matching hybrid/variety characterization trial profile, generate a probability density function; and compute a probability of fit. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to: receive environmental data; analyze the environmental data; select data corresponding to a matching hybrid characterization trial profile; generate a probability density function; and compute a probability of fit for the hybrid/variety. A computer-implemented method for providing product performance visualizations includes receiving environmental data; analyzing the environmental data; selecting data corresponding to a matching hybrid characterization trial profile; generating a probability density function; and computing a probability of fit for the hybrid/variety.