Hydroponic Grow Module Weighing via Tipping and Water Deconvolution
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Solution Overview
Problem
In large scale hydroponic grow systems, accurately determining the weight of individual plants is challenging without interfering with the grow process, as existing technologies struggle to deconvolve plant weight from the combined weight of grow module hardware and available water.
Innovation Solution
A system and method that utilize a tipping module to allow water to settle to a weight equilibrium, record weight measurements, determine the module tipping angle, and use machine learning models to transform these measurements into a predicted water amount, thereby obtaining the plant weight by subtracting the water amount from the total module weight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional weighing methods are used to measure plant weight, then measurement precision is improved, but the grow process is interfered with and scaling to large spaces is limited
Solution Approach 1:
The patent replaces traditional mechanical weighing systems with an imaging-based measurement system. Cameras capture images of plants, and machine learning algorithms analyze these images to estimate plant weight, eliminating the need for physical contact or removal of plants from the grow system.
Solution Approach 2:
The patent introduces an intermediary computational model that translates visual information from images into weight estimates. This intermediary layer allows indirect measurement of plant weight through image analysis rather than direct mechanical weighing, avoiding interference with the grow process.
2Measurement precision
If traditional weighing methods are used to measure plant weight, then measurement precision is improved, but device complexity and scalability are worsened
Solution Approach 1:
The patent replaces complex mechanical weighing infrastructure with simpler imaging devices and computational algorithms. This substitution reduces device complexity while maintaining measurement capability across large-scale grow spaces.
Solution Approach 2:
The patent uses optical copies (images) of plants as proxies for direct weight measurement. By creating and analyzing digital representations of plants through imaging, the system avoids the complexity of physical weighing mechanisms while achieving scalable measurement.
3Measurement precision
If traditional weighing methods are used to measure plant weight, then measurement precision is improved, but productivity is worsened due to time-consuming manual processes
Solution Approach 1:
The patent enables continuous measurement of plant weight through automated imaging and processing. Multiple images can be captured and analyzed in sequence without interrupting the grow process, allowing for ongoing monitoring of plant growth and weight changes.
Solution Approach 2:
The patent implements self-service measurement where the system automatically captures images, processes them through machine learning models, and generates weight estimates without requiring manual intervention. This automation significantly improves measurement efficiency and productivity.
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
This approach allows for efficient and accurate determination of plant weight in large scale hydroponic systems, enabling precise management of water levels and estimation of harvestable product, while minimizing interference with the grow process.
Implementation Method 1
picking up a tipping module to allow water to move while being tipped until the water settles to a weight equilibrium
Data Source
AI summary
Hardware and computational systems for measuring plant weight in hydroponic grow systems. The combined weight of hydroponic grow modules that grow plants using a small amount of water covering the roots is accessible via automated robotic systems. The individual weights of grow infrastructure, plant mass, and available water are convolved. The amount of water in a growing tray can be estimated separately by slightly tipping the module, allowing water to move to one side, and measuring the weight at each of the corners of the module. After controlling for unevenness of the surface where the module is held, a machine learning model predicts the amount of water in a grow module and, subsequently, the plant mass. Reliable estimation of plant mass and water volume allows for both maintenance of precise amounts of water in growing trays and estimation of harvestable product in the grow space.