Seed Recommendation Clustering Using Genetic Embeddings
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Solution Overview
Problem
Existing seed recommendation systems struggle to efficiently process large genetic data sets of seed varieties, as comparing billions of data points manually or using current computer methods is resource-intensive and does not scale easily, making it difficult to recommend genetically similar seed varieties for planting.
Innovation Solution
An artificial-intelligence based algorithm is used to generate embeddings of genetic data with reduced dimensionality, cluster similar seed products, and recommend seeds based on agronomic data, enabling efficient processing and deployment at-scale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or traditional computer methods are used to compare genetic data of seed varieties, then genetic similarity can be identified, but the process becomes resource-intensive and does not scale easily
Solution Approach 1:
The patent replaces traditional mechanical computer-based comparison methods with an AI-based system that uses machine learning algorithms to automatically analyze and compare genetic sequences. This substitution enables efficient processing of large-scale genetic data while maintaining accurate identification of genetically similar seed varieties, resolving the contradiction between measurement precision and productivity.
2Productivity
If AI-based algorithms are used to process genetic data at-scale, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary AI processing layer that sits between the raw genetic data and the final recommendations. This intermediary system uses trained machine learning models to transform complex genetic sequences into meaningful comparisons, enabling efficient at-scale processing while managing algorithmic complexity through pre-trained models and standardized processing pipelines.
3Reliability
If comprehensive genetic data is analyzed to recommend genetically similar seeds, then recommendation accuracy improves, but data processing requirements increase
Solution Approach 1:
The patent extracts and focuses on specific key genetic markers and relevant features from comprehensive genetic data, rather than processing entire genomes. By taking out only the most informative genetic elements for comparison, the system maintains high recommendation accuracy while significantly reducing computational resource consumption and energy requirements.
Data Source
AI summary
A system for generating a seed recommendation is disclosed. The system includes a processor, a display, and a memory. The processor may be configured to retrieve genetic data having a first dimensionality; generate embeddings corresponding to the genetic data, the embeddings having a second dimensionality lower than the first dimensionality; categorize the embeddings into one or more clusters, such that genetically similar seed products are assigned to the same cluster based on the embeddings of the genetically similar seed products; using agronomy data, assign additional seed products to the one or more clusters; generate a recommendation to a grower to plant a first seed categorized in a first cluster, when the grower has previously planted a second seed in the first cluster; and cause the display to display the generated recommendation to the grower.