Multi-Model Crop Simulation Architecture for Trait Prediction
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Solution Overview
Problem
The challenge is to accurately simulate crop traits, such as ear height, to determine the suitability of seed products for specific fields, as inconsistent traits can lead to diminished harvests due to compatibility issues with harvesting equipment.
Innovation Solution
A simulation architecture that combines genetic data of seed products with environmental data from fields, including weather and soil conditions, to accurately predict crop traits using a multi-modal model layer and aggregation layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical performance data is used to select seed products for fields, then selection decisions can be made based on available information, but the accuracy and reliability of predictions are insufficient for precise crop trait simulation
Solution Approach 1:
The system segments the prediction problem into multiple independent models, each specialized for processing a specific data mode (genetic data, environmental data, management data). Each model processes its specific data type through dedicated neural network layers, allowing precise extraction of relevant features from each data source without interference from other data types.
Solution Approach 2:
The system merges the outputs of multiple specialized models through an aggregation layer that combines latent feature representations from genetic, environmental, and management data models. This fusion of information from diverse data sources enables comprehensive and accurate prediction of crop traits by integrating multiple perspectives on the same prediction problem.
2Device complexity
If a single model processes all data types, then the system structure is simpler, but the ability to accurately process and combine different data modes (genetic, environmental, management) is compromised
Solution Approach 1:
The system divides the processing function into separate specialized models, each designed to handle specific data modes appropriately. The genetic data model processes DNA sequences and markers, the environmental data model processes weather and soil information, and the management data model processes agricultural practices. This segmentation allows each model to be optimized for its specific data type while maintaining overall system reliability.
Solution Approach 2:
The aggregation layer serves as a universal component that receives and integrates outputs from all specialized models regardless of their specific functions or data types. This multi-functional aggregation mechanism maintains system manageability while enabling comprehensive trait prediction through the synthesis of diverse data sources.
3Measurement precision
If limited data is available for training, then the system can be simpler and faster to implement, but the accuracy of trait simulation for specific fields is insufficient
Solution Approach 1:
The system creates and trains multiple specialized models that each learn from the available data to represent different aspects of crop trait determination. By copying and replicating the learning process across multiple specialized models (genetic, environmental, management), the system maximizes the extraction of predictive patterns from limited training data, with each model contributing specialized knowledge to the overall prediction.
Solution Approach 2:
The system transforms limited training data into a multi-dimensional representation space through the specialized models, where each model adds a different dimension of understanding (genetic dimension, environmental dimension, management dimension). This dimensional expansion allows the aggregation layer to synthesize comprehensive predictions from data that would be insufficient for any single model alone.
Data Source
AI summary
Systems and methods are provided for simulating one of more traits associated with seed products in multiple fields. One example computer-implemented method includes retrieving, by a simulation computing device, from a data structure, data specific to multiple seed products and multiple fields, where the data includes a first mode of data and a second mode of data, and simulating a trait of interest for the multiple seed products and/or multiple fields, based on a simulation architecture. The simulation architecture includes a first model specific to the first mode of data, a second model specific to the second mode of data, and a neural network coupled to the output of the first and second models as an aggregation layer. The computer-implemented method also includes transmitting the simulated trait of interest for the multiple seed products and/or multiple fields to a grower and/or an agricultural device associated with the multiple fields.