Indoor Agriculture Experiment Selection Using Genetic Algorithms
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Solution Overview
Problem
Current agricultural experimentation in indoor environments is inefficient and costly due to the need to manipulate numerous environmental variables, which can take months or years to optimize crop growth, leading to protracted resource investment and high expenses.
Innovation Solution
The implementation of machine learning, specifically a genetic algorithm approach, to prioritize and schedule experimentation online, optimizing environmental setpoints in indoor agriculture by continually refining recommendations based on intermediate results, reducing the number of necessary treatments and associated time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional exhaustive experimentation is used to manipulate all environmental factors, then complete optimization coverage is achieved, but the number of experiments becomes intractably large and expensive
Solution Approach 1:
The patent applies partial action by using machine learning to identify and prioritize only the most influential environmental factors and their optimal interactions, rather than exhaustively testing all possible combinations. This selective approach achieves sufficient optimization coverage while dramatically reducing experiment complexity and resource requirements.
Solution Approach 2:
The patent transforms the experimental approach by changing from a static exhaustive search to a dynamic machine learning-driven process. The system continuously learns from intermediate results and adapts the experimental parameters and priorities, enabling efficient optimization without requiring complete enumeration of all factor combinations.
2Manufacturing precision
If all environmental factors are manipulated to try all different possible environments, then optimal conditions are found, but the time required takes months or years
Solution Approach 1:
The patent applies preliminary action by using machine learning to predict and prioritize the most promising experimental conditions before conducting physical experiments. The system pre-processes the search space by identifying likely optimal regions based on intermediate results, allowing the experiment to converge on accurate optimal conditions much faster than exhaustive methods.
Solution Approach 2:
The patent implements continuous feedback loops where intermediate experimental results are fed back into the machine learning model, which then refines its predictions and prioritizes subsequent experiments. This iterative learning process dramatically accelerates convergence to optimal conditions while maintaining high accuracy, reducing experimentation time from months or years to a fraction of that duration.
3Reliability
If more experiments are conducted to ensure optimal results, then confidence in optimization increases, but resource investment and expenses increase
Solution Approach 1:
The patent applies self-service by implementing an automated machine learning system that autonomously prioritizes, schedules, and analyzes experiments without requiring extensive human intervention or resource allocation for manual planning. The system efficiently allocates resources to the most promising experiments based on real-time learning, maintaining high optimization confidence while minimizing unnecessary resource consumption.
Data Source
AI summary
Systems, methods, and computer-readable media are provided for determining treatments to apply to plants within control volumes having controlled agricultural environments. Each treatment comprises application of a set of setpoints, choosing a reproduction operation for the treatment, and selecting one or more previous sets from setpoints from one or more previously applied treatments, for use with the chosen reproduction operation.