Hybrid Seed Selection System Optimizing Yield and Risk
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Solution Overview
Problem
Agricultural growers face challenges in selecting the most suitable hybrid seeds for specific fields due to varying environmental conditions, leading to suboptimal yield performance and increased risk from unforeseen environmental fluctuations.
Innovation Solution
A computer system that generates a set of target hybrid seeds with high probability of success by analyzing agricultural data records and geo-location data, normalizing yield values, and classifying environmental conditions to recommend seeds that exceed average yield expectations and manage risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hybrid seeds are selected based on general yield ratings without considering specific field conditions, then seed selection process is simple, but yield performance is suboptimal
Solution Approach 1:
The system transforms the seed selection process by changing parameters from general yield ratings to field-specific performance predictions. It incorporates multiple parameters including environmental conditions, soil characteristics, weather data, and historical yield information to generate customized seed recommendations for each field, thereby improving yield performance while managing complexity through automated analysis.
Solution Approach 2:
The system performs preliminary analysis of field conditions, environmental factors, and hybrid seed characteristics before the planting decision is made. By pre-processing and evaluating multiple data sources and generating seed performance predictions in advance, the system enables informed seed selection that optimizes yield potential while simplifying the actual selection process for growers.
2Reliability
If a diversified planting strategy is used to overcome environmental fluctuations, then risk management is improved, but yield consistency becomes more difficult to predict
Solution Approach 1:
The system applies local quality by providing customized seed recommendations for each specific field based on its unique characteristics. Instead of applying a uniform diversified strategy across all fields, the system analyzes local environmental conditions, soil properties, and historical data for each field to generate tailored seed selections that optimize both risk management and yield predictability for that specific location.
Solution Approach 2:
The system incorporates feedback mechanisms by analyzing historical yield data, environmental conditions, and seed performance across multiple seasons. This feedback loop enables the system to refine its predictions and recommendations, improving both risk management and yield prediction accuracy by learning from past performance patterns and adjusting future recommendations accordingly.
3Productivity
If hybrid seeds are chosen to match optimal growing conditions, then yield potential is maximized, but adaptability to environmental fluctuations is reduced
Solution Approach 1:
The system applies dynamics by considering both the optimal growing conditions for each hybrid seed and the variability of environmental conditions. It dynamically evaluates how well each seed variety is expected to perform under predicted environmental fluctuations, selecting seeds that not only match current optimal conditions but also demonstrate resilience and adaptability to expected variations in weather, soil moisture, and other environmental factors throughout the growing season.
Data Source
AI summary
Techniques are provided for generating a set of target hybrid seeds with optimal yield and risk performance, including a server receiving a candidate set of hybrid seeds along with probability of successful yield values, associated historical agricultural data and property information, and selecting a subset of the hybrid seeds that have probability of success values greater than a filtering threshold. The server generates representative yield values for hybrid seeds based on the historical agricultural data and risk values for each hybrid seed. The server generates a dataset of target hybrid seeds for planting based on the risk values, the yield values, and the properties for the target fields. The dataset of target hybrid seeds includes target hybrid seeds that meet a specific threshold for a range of risk values. The server causes display of the dataset of target hybrid seeds including yield values and risk values for the target fields.