Dynamic Edge-Cloud Allocation for Agricultural State Machines
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Solution Overview
Problem
Designing and implementing complex machine learning-based data processing pipelines in agriculture is hindered by the need for significant engineering and data science expertise, which is uncommon in the agricultural industry, limiting the adoption of precision agriculture techniques like machine learning-based phenotyping.
Innovation Solution
A platform-independent agricultural state machine is designed using a graphical user interface that allows growers to visually create machine learning-based phenotyping systems, with computational aspects dynamically allocated between edge and cloud computing resources based on resource capabilities and constraints, abstracting the implementation details from the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning-based data processing pipelines are implemented in agriculture, then phenotyping accuracy and precision agriculture capability are improved, but the requirement for engineering and data science expertise increases
Solution Approach 1:
The patent introduces an intermediary system that translates high-level phenotyping specifications from growers into detailed machine learning pipeline configurations. This intermediary layer handles the complexity of model selection, parameter tuning, and computational resource management, allowing growers to specify phenotyping requirements without needing deep expertise in machine learning engineering.
Solution Approach 2:
The system segments the complex machine learning pipeline into modular, independently configurable components. Each component (data ingestion, preprocessing, model inference, post-processing) can be separately optimized and managed, reducing the overall complexity barrier while maintaining high phenotyping accuracy through specialized handling of each segment.
2Adaptability or versatility
If complex machine learning pipelines are implemented, then phenotyping capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal phenotyping platform that can handle multiple crop types and phenotyping tasks through a single integrated system. The system provides standardized interfaces and workflows that adapt to different phenotyping requirements without requiring separate complex pipeline configurations for each use case, thereby reducing system complexity while maintaining versatility.
3Speed
If edge computing resources are used for machine learning operations, then latency is reduced, but computational capability is limited
Solution Approach 1:
The patent segments the machine learning workload and distributes different portions between edge and cloud computing resources. Time-critical inference operations that require low latency are executed at the edge on agricultural vehicles, while computationally intensive tasks such as model training and complex data processing are offloaded to cloud resources, optimizing the trade-off between speed and computational capability.
Data Source
AI summary
Implementations are disclosed for dynamically allocating aspects of platform-independent machine-learning based agricultural state machines among edge and cloud computing resources. In various implementations, a GUI may include a working canvas on which graphical elements corresponding to platform-independent logical routines are manipulable to define a platform-independent agricultural state machine. Some of the platform-independent logical routines may include logical operations that process agricultural data using phenotyping machine learning model(s). Edge computing resource(s) available to a user for which the agricultural state machine is to be implemented may be identified. Constraint(s) imposed by the user on implementation of the agricultural state machine may be ascertained. Based on the edge computing resource(s) and constraint(s), logical operations of some platform-independent logical routines may be dynamically allocated to the edge computing resource(s), and logical operations of other platform-independent logical routines may be dynamically allocated to a cloud computing resource.