Hybrid Seed Portfolio Selection for Field-Specific Yield Risk
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Solution Overview
Problem
Existing agricultural practices struggle to optimize hybrid seed selection and planting strategies to achieve optimal yield and mitigate environmental fluctuations, as field conditions often do not align with the optimal growing conditions for specific hybrid seeds.
Innovation Solution
A computer system that generates a target success yield group of hybrid seeds by analyzing agricultural data and geo-location data to identify seeds with a high probability of successful yield, using hybrid seed normalization, probability of success generation, and yield classification to create a subset of seeds exceeding average yield expectations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hybrid seeds are selected based on general yield ratings, then average yield potential is achieved, but field-specific optimal conditions are not met resulting in suboptimal actual yield
Solution Approach 1:
The system applies local quality by matching specific hybrid seeds to specific field conditions. Instead of using a uniform seed selection approach, the system analyzes individual field characteristics (soil type, climate, topography) and recommends hybrid seeds optimized for each specific field's conditions, thereby achieving optimal yield for each location rather than relying on general yield ratings
Solution Approach 2:
The system performs preliminary action by conducting field condition analysis and hybrid seed matching before planting. The computer system evaluates field characteristics, soil properties, and climate data in advance to identify the most suitable hybrid seeds, allowing growers to make informed decisions prior to seed selection and planting, thus avoiding suboptimal yield outcomes
2Reliability
If a diversified planting strategy is used to mitigate environmental fluctuations, then risk is reduced, but yield consistency across different field conditions becomes harder to optimize
Solution Approach 1:
The system applies parameter changes by using computer-based analysis to evaluate multiple field parameters (soil composition, climate data, topography, historical yield) and hybrid seed characteristics simultaneously. This data-driven approach optimizes the planting strategy by identifying the specific combination of hybrid seeds and field conditions that will achieve consistent yield across diverse fields, reducing the complexity of manual diversification planning
Solution Approach 2:
The system implements feedback mechanisms by analyzing historical yield data and field performance information to continuously improve hybrid seed recommendations. The computer system uses past performance data to refine its matching algorithms, ensuring that diversified planting strategies are based on proven performance patterns rather than guesswork, thereby achieving yield consistency while managing complexity
Data Source
AI summary
Systems and methods are provided for managing hybrid seeds for planting. One example computer-implemented method includes receiving a first dataset of hybrid seeds for planting on one or more target fields, and selecting a subset of the hybrid seeds based at least on environmental classification data for the hybrid seeds, location data for the one or more target fields, and one or more properties of the plants grown from the hybrid seeds. The method also includes generating a representative yield value for each hybrid seed in the subset of hybrid seeds based on historical yield data for the seeds and generating risk values for the subset of hybrid seeds based on yield variability of the hybrid seeds over time as indicated in the historical yield data. The method further includes generating a second dataset of hybrid seeds for planting based on the risk values and the representative yield values.