Agricultural ML Model Deployment for Customized Field Recommendations
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Solution Overview
Problem
Current agricultural systems lack customization and scalability in generating seed and planting recommendations, often requiring extensive computational resources and struggling to manage numerous customized models, making it difficult to track and catalog various versions for farmers.
Innovation Solution
A computer-based platform for executing machine learning algorithms that includes a model repository, model execution infrastructure, and model history log, allowing for the storage, cataloging, and execution of machine learning models, enabling customization and management of agricultural models, and providing specific recommendations to crop growers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computer-based systems execute digital models on copious amounts of agricultural data, then seed and planting recommendations can be generated, but enormous computational resources are required
Solution Approach 1:
The patent segments the model execution process by deploying machine learning models directly to edge devices (agricultural machines, field computers, mobile devices) rather than centralizing computation in cloud data centers. This distributes computational workload across multiple localized nodes, reducing the energy burden on any single system while maintaining recommendation accuracy through local data processing.
Solution Approach 2:
The system performs preliminary actions by pre-configuring and storing multiple customized machine learning models in a model repository before field deployment. These pre-trained models are then selectively executed on edge devices based on specific field conditions, avoiding the need to process copious amounts of agricultural data through complex central systems for each recommendation query.
2Adaptability or versatility
If customizations of agricultural models are enabled, then specific recommendations can be generated, but managing and maintaining the models becomes problematic
Solution Approach 1:
The patent implements a universal model repository system that can store, manage, and deploy multiple types of customized machine learning models through a single centralized interface. This multi-functional platform handles diverse model formats, versions, and configurations uniformly, allowing farmers to access customized recommendations without dealing with the underlying complexity of model management.
Solution Approach 2:
The centralized model repository acts as an intermediary between model creators and end users. It manages the complexity of storing, versioning, and deploying customized models while presenting a simplified interface to farmers. The repository mediates between the need for model customization and the desire for easy management by handling model lifecycle operations centrally.
3Adaptability or versatility
If numerous customized models are created, then specific agricultural recommendations can be provided, but tracking and cataloging the various versions becomes difficult
Solution Approach 1:
The system implements automated feedback mechanisms that track model execution performance, version updates, and deployment status across the distributed network. The centralized model repository receives feedback from edge devices about model performance and usage, automatically updating catalogs and version records without manual intervention, thus preventing information loss about model versions.
Solution Approach 2:
The model repository system performs self-service by automatically cataloging, versioning, and tracking all customized models in the distributed network. When models are deployed to edge devices or updated, the system autonomously records version information, maintains metadata, and updates the central catalog, eliminating the need for manual tracking and preventing version information loss.
Data Source
AI summary
A computer-implemented data processing method providing an improvement in executing machine learning processes on digital data representing physical properties related to agriculture is described. In an embodiment, the method comprises: receiving, from a computing device, a request to browse machine learning models stored in a digital model repository; retrieving, from the digital model repository and transmitting to the computing device, information about the machine learning models stored in the digital model repository; receiving, from the computing device, a selection, from the machine learning models, of a particular model and receiving particular input for the particular model; using resources available in a model execution infrastructure platform, executing the particular model on the particular input to generate particular outputs; transmitting the particular output to a computer configured on an agricultural machine to control the agricultural machine as the agricultural machine performs agricultural tasks in an agricultural field.