Autonomous Indoor Farming Control With Robotic Vertical Access
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Solution Overview
Problem
Current vertical indoor farming systems are expensive, inefficient in space utilization, and lack autonomy, as they require extensive environmental control systems and human access, which limits space for plant growth and cannot adapt to plant conditions or learn from previous growing cycles.
Innovation Solution
An autonomous farming system that includes a computing device with a machine learning model to collect and analyze plant and environmental data, adjusting parameters like lighting, irrigation, and air circulation to optimize growing conditions, and uses an articulated robot to access and manage plant containers efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If environmental control systems and shelving racks are installed in indoor farming systems, then plant growing conditions can be controlled, but space utilization is reduced due to human access requirements and system complexity increases
Solution Approach 1:
The system transitions from horizontal shelving racks requiring human access to vertical stacked grow containers that can be accessed by robotic arms from the side, effectively utilizing vertical space and eliminating the need for human operators to walk through the facility
Solution Approach 2:
The system implements autonomous robotic arms that automatically perform monitoring, harvesting, and container manipulation tasks, eliminating the need for human operators and maximizing space utilization for plant growth
2Adaptability or versatility
If traditional vertical indoor farming systems are implemented, then controlled environment agriculture benefits are achieved, but the systems cannot autonomously adapt to plant conditions or learn from previous growing cycles
Solution Approach 1:
Sensors continuously monitor plant health indicators including leaf color, temperature, and humidity, feeding this data back to the control system which automatically adjusts environmental parameters and triggers remedial actions when deficiencies are detected
Solution Approach 2:
The system uses machine learning models to autonomously analyze plant conditions, determine deficiencies, and implement corrective measures without human intervention, enabling the system to learn and adapt from previous growing cycles
3Productivity
If extensive sensor and control systems are deployed for environmental control, then growing conditions can be optimized, but implementation costs increase significantly
Solution Approach 1:
The system uses multi-functional robotic arms that can perform multiple tasks including monitoring, harvesting, and container manipulation, reducing the need for separate specialized equipment and lowering overall system complexity and cost
Data Source
AI summary
An autonomous farming system includes a computing device that is configured to obtain plant characteristic data that characterizes one or more plant characteristics of a plant growing in at least one growing module. The computing device is further configured to determine at least one growing deficiency of the plant based on the plant characteristic data using a farming engine and to send at least one farming action to a farming controller operatively coupled to the at least one growing module. The at least one farming action includes a remedial action to improve growing conditions of the plant.