Autonomous Nutrient Dosing is an AI-driven system that continuously monitors and adjusts the nutrient solution in hydroponic systems to ensure optimal plant growth conditions.
Inconsistent nutrient levels leading to suboptimal plant growth and potentially higher water usage due to over-fertilization or under-fertilization.
The technology uses closed-loop sensors to measure ion levels in the nutrient solution. Machine learning models analyze these data points in real-time to determine the precise amounts of micro-nutrients needed, which are then injected into the system via controlled dosing mechanisms.
Manufacturing involves developing high-precision sensors, integrating them with AI algorithms, and creating robust dosing systems that can operate in a controlled environment without human intervention.
The process includes designing the hardware components for sensor placement, data collection, and nutrient injection. Software development focuses on training ML models to interpret sensor data accurately and make timely adjustments.
Field units draw low hundreds of watts; fabrication is energy-intensive due to vacuum baking processes required for certain components.
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