Hybrid Growing Environment With AI Crop Feedback Control
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Solution Overview
Problem
Controlled Environment Agriculture (CEA) systems face challenges such as high risk of crop failure, low product flexibility, and high operational costs due to mechanical dependence, while vertical farms struggle with inefficient use of space and resources.
Innovation Solution
A hybrid growing environment using mixed lighting, optimized climate control, and AI-driven sensor systems to adjust variables based on real-time plant growth stages and market demand, incorporating both vertical and horizontal space utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vertical farming with densely packed crops and artificial lighting is implemented, then yield per square foot increases and water usage decreases, but capital expenditures and operational costs increase due to heavy mechanical dependence
Solution Approach 1:
The system dynamically adjusts lighting intensity and spectral composition based on real-time plant growth stage detection using computer vision. The lighting system transitions from static to adaptive control, modifying parameters such as photoperiod, intensity, and spectrum to match specific developmental phases of the plants, thereby optimizing growth while reducing energy waste.
Solution Approach 2:
The system implements closed-loop feedback through continuous monitoring of plant health metrics using sensors and computer vision. Growth parameters such as leaf area, plant height, and biomass are measured and fed back to the control system, which automatically adjusts environmental conditions including lighting, temperature, and nutrient delivery to maintain optimal growth trajectories.
2Loss of substance
If controlled environment agriculture is implemented, then water usage and land area are reduced, but operational costs increase due to high capital expenditures and mechanical dependence
Solution Approach 1:
The system employs autonomous robotic platforms equipped with computer vision and AI to perform planting, monitoring, and harvesting operations. These self-service mechanisms reduce the need for manual labor and complex mechanical infrastructure, thereby lowering operational costs while maintaining the water-efficient benefits of controlled environment agriculture.
Solution Approach 2:
Traditional mechanical irrigation and fertilization systems are replaced with precision delivery mechanisms controlled by sensor networks and AI algorithms. Nutrient solutions and water are delivered directly to plant roots based on real-time demand assessment, eliminating the need for extensive piping and mechanical distribution infrastructure.
3Ease of operation
If traditional farming with plants spread apart is implemented, then plants have adequate growth space, but large amounts of unused white-space remain until maturity
Solution Approach 1:
The system transitions from two-dimensional horizontal spacing to three-dimensional vertical stacking arrangements. Multiple layers of plants are cultivated at different heights within the same footprint, with each layer receiving customized lighting and environmental conditions. This dimensional transition eliminates unused space while providing adequate growth volume for each plant.
Solution Approach 2:
Each plant or plant group receives customized environmental conditions tailored to its specific growth stage, species requirements, and positional characteristics. Lighting intensity, spectrum, temperature, and nutrient delivery are locally optimized for each zone rather than applying uniform conditions across the entire facility, maximizing space utilization efficiency.
4Device complexity
If fixed growing conditions are maintained in CEA, then environmental control is simplified, but product flexibility and adaptability to market demand decrease
Solution Approach 1:
The system dynamically reconfigures environmental parameters including lighting schedules, temperature setpoints, humidity levels, and nutrient formulations in response to real-time plant growth stage detection and market demand signals. This enables flexible adaptation of crop varieties, growth rates, and harvest timing without requiring permanent infrastructure changes.
Solution Approach 2:
The controlled environment facility is designed to support multiple crop types and growth modes within the same space. The system can switch between different plant species, vertical stacking configurations, and growing methodologies (such as hydroponics, aeroponics, or soil-based) using the same infrastructure, thereby maximizing product flexibility and market responsiveness.
Data Source
AI summary
A method for optimizing plant growth in a hybrid growing environment may implement artificial intelligence to measure and alter plant biometrics. Independent variables may be altered by a control unit by manipulating various control systems within the growing environment. Dependent variables may be measured, and the response of the dependent variables may be recorded in association with the alteration to the independent variables. A historical database may store data regarding the variables and may be referenced and updated by an exemplary embodiment. The control unit may optimize one or more variables or parameters based on a targeted value or outcome. An exemplary hybrid growing environment may include one or more vertical growing phases and a horizontal phase. The phases may implement various watering systems and may be hydroponic, be a complete CEA, use a mixture of natural, artificial, and/or supplemental lighting.