Greenhouse IoT Control for Produce Maturity Timing
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Solution Overview
Problem
Current greenhouse environment control systems lack the ability to dynamically adjust growth rates of fresh produce to match market demand, leading to significant food waste due to mismatched supply and demand in the supply chain.
Innovation Solution
A method and system that utilize IoT data from an Internet-of-Things network to forecast demand and adjust growing environment conditions in greenhouses, such as temperature and maturity times, to align produce availability with predicted demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If historical data is used for supply chain planning and demand forecasting, then planning and forecasting can be performed, but food waste occurs due to inability to dynamically adjust supply to match demand
Solution Approach 1:
The system implements a closed-loop feedback mechanism where IoT sensors continuously monitor supply chain conditions (temperature, humidity, location, produce quality), this data is processed by analytics engines that generate real-time demand forecasts, which then feed back to adjust greenhouse growing parameters. This continuous feedback loop enables dynamic adaptation to actual demand signals, preventing overproduction and food waste.
Solution Approach 2:
The greenhouse system performs self-adjustment of growing conditions based on automated demand forecasting. The system autonomously modifies environmental parameters (temperature, humidity, light, irrigation) without manual intervention, allowing the supply chain to self-regulate produce output to match predicted demand, thereby reducing food waste from mismatched supply.
2Adaptability or versatility
If greenhouse environment is statically controlled, then growing conditions are stable, but supply cannot be adjusted to meet market demand
Solution Approach 1:
The greenhouse control system transitions from static to dynamic operation, continuously adjusting environmental parameters based on real-time data. Temperature, humidity, irrigation, and lighting systems are dynamically modified according to demand forecasts and produce growth stages, enabling the greenhouse to adapt its output timing and quantity to match market demand while maintaining controlled growing conditions.
Solution Approach 2:
The system changes multiple growing parameters simultaneously (temperature setpoints, humidity levels, irrigation rates, light intensity) based on processed supply chain data. By dynamically adjusting these physical parameters in response to demand signals, the greenhouse can control produce maturity timing and quantity to align with market requirements without requiring complete system redesign.
3Productivity
If produce growth rate is increased to meet demand, then supply increases, but maturity time changes affect produce quality and timing alignment
Solution Approach 1:
The system performs preliminary adjustments to growing conditions based on forecasted demand before produce reaches critical maturity stages. By pre-modifying environmental parameters in response to predicted demand signals, the system steers produce development toward target maturity timing, ensuring both adequate supply quantity and precise timing alignment with market demand without compromising quality.
Solution Approach 2:
The system dynamically modifies growing parameters (temperature, CO2 levels, irrigation) to control the rate of produce development. By adjusting these parameters in response to demand forecasts, the system can accelerate or decelerate growth to achieve target output quantities while maintaining precise control over maturity timing, balancing productivity increases with timing precision.
Data Source
AI summary
A method, computer program product, and a system are configured to: detect sensors in an Internet-of-Things (IoT) network of a supply chain of fresh produce; collect data from the detected sensors via the IoT network; determine a demand of a type of produce at a future date using the collected data and a forecasting model; determine a change to an output of a greenhouse growing the type of produce based on the determined demand for the type of produce; determine a growing plan for the greenhouse based on the determined change to the output of the greenhouse; adjust one or more growing environment conditions in the greenhouse based on the growing plan, wherein the adjusting includes sending control signals to one or more computer-based environment control systems in the greenhouse, and wherein the adjusting changes a maturity time of a batch of the type of produce growing the greenhouse.