Dynamic Phenotyping Priority Selection in Plant Breeding
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Solution Overview
Problem
Accurate and precise trait phenotyping is a major limiting factor in plant breeding, particularly due to the impracticality of measuring every possible trait phenotype across multiple, geographically separated locations with varying environmental conditions, which hinders effective trait mapping and molecular breeding.
Innovation Solution
A method and system that utilize soil and environmental data to predict crop conditions, prioritize trait phenotyping measurements by identifying locations with high likelihood of phenotypic variation, and deploy resources efficiently through a crop model integrated with soil sampling, weather stations, and analysis engines to collect critical data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trait phenotype measurements are collected at all locations, then measurement completeness is improved, but resource requirements and time consumption increase significantly
Solution Approach 1:
The system changes the parameter of measurement selection from static (all locations) to dynamic (priority-based selection). By calculating a phenotyping priority score based on multiple parameters including genetic variation, environmental conditions, and crop model predictions, the system determines which locations warrant measurement resources at any given time, optimizing both accuracy and efficiency
Solution Approach 2:
The system performs preliminary calculations of phenotyping priority scores and environmental conditions before deploying measurement resources. By pre-assessing which locations are most likely to yield valuable phenotypic data based on genetic and environmental factors, the system prepares advance measurement targets, avoiding wasted resources on locations unlikely to produce meaningful results
2Reliability
If environmental monitoring is performed continuously at all locations, then environmental data accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system employs a universal crop model that can be applied across all locations and multiple crop types. This single model framework handles diverse environmental inputs (soil data, weather data, genetic information) and produces standardized phenotyping priority outputs, reducing the need for location-specific complex models while maintaining reliability
Solution Approach 2:
The crop model serves as an intermediary between raw environmental data and phenotyping decisions. Instead of directly processing complex environmental data at each location, the system uses the crop model to translate environmental conditions into phenotyping priority scores, simplifying the overall system architecture while improving data interpretation reliability
3Loss of time
If phenotyping measurements are taken early in the season, then timing for trait expression is improved, but environmental conditions may not yet be optimal for variation expression
Solution Approach 1:
The system dynamically adjusts phenotyping timing and location selection throughout the growing season. By continuously updating phenotyping priority scores based on current environmental conditions, genetic data, and crop model predictions, the system determines the optimal moment to phenotype at each location, adapting to changing conditions rather than following a fixed schedule
Solution Approach 2:
The system uses feedback from environmental monitoring, genetic analysis, and crop model predictions to continuously refine phenotyping decisions. By monitoring environmental conditions and updating phenotyping priority scores in real-time, the system receives feedback on which locations and timepoints are most likely to capture meaningful trait variation, adjusting measurements accordingly
Data Source
AI summary
A method for targeting trait phenotyping of a plant breeding experiment includes collecting soil data for at least one location, applying the soil data to a crop model, performing environmental monitoring at the at least one location to generate environmental data, updating the crop model with the environmental data, and using the crop model to provide predicted crop conditions. The method further includes determining environmental conditions for each of the plant breeding experiments, determining a likelihood of trait phenotype variations for each experiment using the environmental conditions and the predicted crop conditions, selecting a subset of the plant breeding experiments for collecting trait phenotype measurements based on the likelihood of trait phenotypic variation, and collecting trait phenotype measurements from the subset of the plant breeding experiments.