Crop Trait Prediction Model for Variety Selection
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Solution Overview
Problem
Current methods for trait development in agricultural crops are inefficient and resource-intensive, as they rely on extensive testing and decision-making processes to predict the expected performance of modified plants.
Innovation Solution
A system and method utilizing a multi-modal architecture that includes a computing device with a trained prediction architecture, a repository of genotypic, weather, and soil data, to identify and select proposed crop varieties based on predicted phenotypic traits and phenotypic gain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive testing and decision-making processes are used to predict expected performance of modified plants, then reliability of trait performance prediction is improved, but productivity and resource efficiency deteriorate
Solution Approach 1:
The system performs preliminary prediction of trait performance using a trained machine learning model before actual field testing. The model predicts phenotypic traits based on genotypic data, weather data, and soil data, allowing researchers to identify promising candidate varieties in silico before allocating physical resources for experimental validation.
Solution Approach 2:
The system creates a virtual copy of the breeding process through simulation. Instead of physically testing all candidate varieties, the system uses a trained prediction model that replicates the behavior and performance characteristics of plants under various environmental conditions, enabling virtual screening of numerous candidates.
2Measurement precision
If extensive testing of proposed varieties is conducted, then measurement precision of trait performance is improved, but loss of time and resource allocation worsen
Solution Approach 1:
The system performs preliminary prediction of multiple traits simultaneously using the trained model before conducting physical experiments. By predicting performance across various environmental conditions and trait types in advance, the system identifies the most promising candidates for detailed experimental validation, reducing the number of varieties that require time-consuming field testing.
Solution Approach 2:
The evaluation process is segmented into two stages: (1) rapid computational screening of many candidate varieties using the prediction model to identify top performers, and (2) focused experimental validation of only the most promising candidates. This segmentation allows comprehensive assessment without requiring extensive testing of all candidates.
3Adaptability or versatility
If the number of proposed varieties to be tested is increased, then adaptability and coverage of genetic diversity are improved, but device complexity and resource requirements worsen
Solution Approach 1:
The system manages genetic diversity through virtual representation rather than physical handling. The prediction model can evaluate any number of candidate varieties by processing their genotypic data, allowing comprehensive coverage of genetic diversity without the logistical complexity of managing and testing physical plant materials for each candidate.
Solution Approach 2:
The prediction system serves multiple functions: it screens for desired traits, predicts performance under various environmental conditions, identifies promising candidates for breeding, and prioritizes varieties for experimental validation. This multi-functional approach allows the system to handle diverse genetic material efficiently through a single integrated platform.
Data Source
AI summary
Example systems and methods are disclosed for use in trait development in agricultural crops. One example computer-implemented method includes identifying multiple proposed varieties of a crop, wherein each of the multiple proposed varieties includes a distinct genetic sequence, as compared to the other ones of the multiple proposed varieties and to known varieties; predict, using a trained model, a trait of interest for each of the multiple proposed varieties based on data included in a repository; select ones of the multiple proposed varieties, based on an acquisition function which is based on phenotypic gain; and cause seeds representative of the selected ones of the multiple proposed varieties to be directed to an experimental phase to assess the trait of interest of the selected ones of the multiple proposed varieties.