Seed Selection Prediction Model Optimizing Yield Accuracy
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Solution Overview
Problem
Growers face inaccuracies in selecting seed types for planting due to unpredictable crop yields and associated risks, leading to potential lower yields from chosen seed types compared to unplanted alternatives.
Innovation Solution
A computer-implemented system that uses seed placement prediction models and optimization models to recommend seed types for planting based on location data, weather conditions, and grower constraints, providing data-driven decisions for seed selection and placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If growers select seed types based on traditional methods without prediction models, then the selection process is simple, but the accuracy of seed selection is low and yields are unpredictable
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical yield data, weather data, and soil characteristics before the planting season to pre-determine optimal seed selections. This advance preparation eliminates the need for complex real-time decision-making during planting while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary prediction model that acts as a mediator between traditional simple selection methods and complex data analysis. This intermediary layer processes multiple data sources (yield history, weather, soil) and presents simplified recommendations to growers, resolving the contradiction between accuracy and complexity.
2Productivity
If growers plant seed types without considering yield prediction and variance, then the planting process is straightforward, but the crop yield performance is suboptimal
Solution Approach 1:
The system changes key parameters including yield prediction accuracy, variance assessment, and their integration into seed selection criteria. By adjusting these parameters and weighting them appropriately in the prediction model, the system optimizes crop yield while managing the complexity of the planting decision process.
3Measurement precision
If growers use detailed prediction models for multiple seed products, then the accuracy of yield prediction improves, but the complexity of the prediction system increases
Solution Approach 1:
The patent segments the prediction system into distinct modules: one for yield prediction and another for variance prediction. Each module processes specific data types and produces specialized outputs that are then integrated. This segmentation maintains high prediction accuracy while managing system complexity through modular design.
4Reliability
If growers make seed selection decisions without optimization models, then the decision-making process is simple, but the risk of lower yields increases
Solution Approach 1:
The system implements feedback mechanisms where actual yield data from previous seasons is fed back into the prediction model to continuously improve future predictions. This feedback loop increases the reliability of seed selection over time while the automated nature of the feedback process prevents excessive complexity accumulation.
Data Source
AI summary
Systems and methods for planting specified seed products in target growing spaces. An example method includes receiving a request for a planting recommendation related to seeding of a target growing space and, in response, determining, using one or more seed placement prediction models, a prediction output including a predicted yield for multiple seed products at the target growing space at each of one or more different weather conditions. The method also includes determining, using an optimization model, a seed planting recommendation output, based on at least the prediction output and at least one grower constraint parameter associated with the target growing space, where the seed planting recommendation output includes at least one of the multiple seed products, and then directing planting of the at least one of the multiple seed products at the target growing space based on the seed planting recommendation output.