AI Hydroponic Nutrient Control Without pH and EC Probes
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Solution Overview
Problem
Existing hydroponic systems, especially in small private greenhouses, lack cost-effective and user-friendly automation for adjusting nutrient solution conditions, requiring expensive industrial probes and technical expertise.
Innovation Solution
A method using artificial intelligence models to control hydroponic systems by obtaining and processing sensor data to approximate additive volumes, allowing for automated adjustment of nutrient solution pH and conductivity without continuous direct measurement, utilizing standard sensors and peristaltic pumps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated dosing with industrial pH and EC probes is used, then the nutrient solution quality is continuously measured and adjusted automatically, but the acquisition cost and operation cost become very high
Solution Approach 1:
The patent replaces expensive industrial pH and EC probes with a copy approach: using standard temperature sensors to measure environmental conditions and an artificial intelligence model to copy the functionality of expensive probes by predicting nutrient solution parameters based on environmental data and plant responses. This eliminates the need for direct measurement with expensive sensors while achieving similar control objectives.
Solution Approach 2:
The patent uses inexpensive standard temperature sensors instead of costly industrial pH and EC probes. These standard sensors are much cheaper to acquire and replace, making automated control economically viable for small-scale hydroponic systems while maintaining the essence of automated adjustment through AI-driven predictions.
2Extent of automation
If industrial pH and EC probes are used for continuous measurement, then automated adjustment is achieved, but technical knowledge for handling and maintenance is required
Solution Approach 1:
The patent simplifies operation by replacing complex industrial probes with standard temperature sensors that are easier to handle and maintain. The AI model copies the sophisticated measurement and adjustment logic, eliminating the need for users to possess specialized technical knowledge about probe calibration, maintenance, and interpretation of pH and EC readings.
Solution Approach 2:
The patent substitutes the mechanical and chemical measurement systems (pH and EC probes requiring technical handling) with an environmental sensing system using standard temperature sensors combined with AI algorithms. This replacement reduces the technical knowledge barrier while maintaining automated adjustment capabilities.
3Quantity of substance
If manual measurement and adjustment is performed, then low cost instruments can be used, but the adjustment process is time-consuming and requires repeated measurements
Solution Approach 1:
The patent enables the system to self-adjust by using standard temperature sensors to continuously monitor environmental conditions and an AI model to automatically determine the appropriate nutrient solution adjustments. This eliminates the need for manual measurement and interpretation by the grower, automating the entire process from sensing to decision-making while using inexpensive sensors.
Solution Approach 2:
The patent implements continuous feedback through standard temperature sensors monitoring environmental conditions, with the AI model processing this data to automatically adjust nutrient solution parameters. This closed-loop feedback system eliminates the need for repeated manual measurements while maintaining low instrument costs, as the AI continuously optimizes based on real-time environmental data.
4Extent of automation
If AI model with multiple sensors is used for prediction, then nutrient adjustment can be automated, but the acquisition price and user education requirements increase
Solution Approach 1:
The patent extracts only the essential environmental parameters needed for nutrient solution control, using standard temperature sensors rather than multiple specialized sensors. The AI model is trained to predict nutrient adjustments based on this minimal sensor input, eliminating the need for expensive multi-sensor configurations while achieving effective automation.
Solution Approach 2:
The patent changes the approach from using multiple specialized sensors (pH, EC, NPK, TDS, oxygen) to using standard temperature sensors with AI-based parameter prediction. The AI model infers nutrient solution parameters from environmental temperature data and plant response patterns, achieving automation with simpler, cheaper sensing equipment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides accurate and automated nutrient solution adjustment with lower costs and operational complexity, suitable for small-scale hydroponic systems, ensuring optimal plant growth conditions.
Implementation Method 1
an automatic response occurs in the form of addition of a small dose of additive, which is transported to the nutrient solution from the reservoir using a peristaltic pump
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
The object of the invention is a method of controlling conditions for a controlled hydroponic system (10). Data from sensors (6) of a training hydroponic system (9) environment are processed by an artificial intelligence model (8), wherein the result is an approximation of the additive volume for the nutrient solution (2). The trained artificial intelligence model (8) in the controlled hydroponic environment (10) determines the additive volume to be added to the nutrient solution (2).