Zonal variability optimization using machine learning in a grow space
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current growspace systems face high labor costs, inefficiency, lack of data granularity, inflexible automation, and difficulty in adapting to crop changes due to expensive retooling and separate R&D facilities, limiting data collection and experimentation.
Innovation Solution
A control space operating system with modular, decoupled automation, robotic transport, centralized processing, and scheduling/monitoring software, enabling zonal control over variables like lighting, humidity, CO2, and nutrient mixtures, with automated data collection and labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional farming equipment is purchased to reduce labor costs, then automation level increases, but capital investment increases significantly
Solution Approach 1:
The system segments the growspace into multiple independently controllable zones, each with its own environmental parameters (lighting, humidity, temperature, CO2). This modular approach allows automation to be implemented incrementally zone-by-zone rather than requiring complete system replacement, reducing capital investment while maintaining high automation levels.
Solution Approach 2:
The system implements self-service through automated monitoring and control where sensors continuously gather data and the system automatically adjusts environmental parameters without human intervention. This eliminates the need for expensive manual labor while using cost-effective automated systems that serve themselves through feedback loops.
2Measurement precision
If granular data collection is implemented to improve crop management, then measurement precision increases, but system complexity and operational burden increase
Solution Approach 1:
The system uses universal multi-functional sensors that can measure multiple parameters (temperature, humidity, light intensity, CO2 levels) simultaneously within each zone. This approach achieves granular data collection without proportionally increasing system complexity, as single sensors perform multiple measurement functions.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is automatically processed and used to adjust environmental controls. This automated feedback mechanism simplifies operations by eliminating manual data analysis while maintaining high measurement precision through continuous monitoring and automatic response.
3Manufacturing precision
If zonal control is implemented to improve crop growth optimization, then manufacturing precision increases, but device complexity increases
Solution Approach 1:
The growspace is divided into discrete zonal units, each with independent control over environmental parameters. This segmentation enables precise control of growth conditions for different crop types or growth stages without requiring a completely complex centralized system, as each zone operates semi-independently with standardized control mechanisms.
Solution Approach 2:
The system achieves precise growth control by independently adjusting key environmental parameters (light intensity, humidity, temperature, CO2 concentration) in each zone based on crop needs. This parameter-based control approach simplifies the control system compared to mechanical or physical modifications, allowing precise manufacturing-level control through software-managed parameter changes.
4Adaptability or versatility
If R&D and production are separated to improve experimentation, then adaptability increases, but loss of time occurs in transferring lessons between facilities
Solution Approach 1:
The system merges R&D and production functions within the same growspace infrastructure by allowing different zones to simultaneously serve as test beds for experimentation and production areas for commercial growth. This integration eliminates knowledge transfer time between separate facilities while maintaining adaptability, as lessons learned in one zone can be immediately applied to adjacent zones or other controlled environments within the same system.
Data Source
AI summary
A control space operating system The system includes a control space with one or more data source zones and a control space manager. The control space manager can collect data and control different variables across different data source zones in order to determine optimal policies and conditions for data source growth and generation.