article · Smart Agricultural Technology
A model predictive control system has been developed for water-saving drip irrigation in greenhouse environments. Designed to maintain soil moisture within field capacity and above wilting point while counteracting evapotranspiration, the system integrates a data-driven predictive model with real-time Internet of Things monitoring. The control algorithm runs on a Raspberry Pi 4, driving an irrigation pump using pulse width modulation to deliver water blended with fertiliser. When evaluated against a conventional automated evapotranspiration-based system using cantaloupe crops, the model predictive controller achieved a superior water productivity index of 36.8 grams per litre compared to 25.6 grams per litre. The resulting fruit also showed higher sweetness, reaching 13.5 Brix against 10.5 Brix for the benchmark system. Although the benchmark system produced a 21.7 percent higher total harvest mass, the predictive controller effectively optimised resource use through event-based scheduling and environmental tracking.
Inefficient irrigation depletes freshwater resources and can impair crop quality through moisture stress. By combining predictive control with low-cost microcomputing and Internet of Things sensors, this approach demonstrates how precision agriculture can significantly boost water productivity and fruit sweetness in protected horticulture, offering a practical method to conserve water in commercial greenhouse cultivation.
This technology offers an automated fertigation management tool for commercial greenhouse operators and precision agriculture equipment providers. Tested in a physical greenhouse on cantaloupe crops using accessible hardware like the Raspberry Pi 4, the system represents an applied and experimentally validated prototype. Commercial deployment would require refining the hardware packaging, assessing yield trade-offs against fruit quality metrics, and integrating user interfaces for standard farm management systems.
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Traditional irrigation control systems is characterized with inefficient management of water and often results in low water productivity index and reduced cultivation yield. In addition, insufficient water supply and high rate of water loss due to evapotranspiration increases plant stress which often affects its growth and development. Therefore, to address this issues, this paper is aimed at developing a model predictive control (MPC) strategy for water saving drip irrigation experiment that will regulate the soil moisture content within the desired field capacity and above the wilting point, while scheduling irrigation to replace the loss of water from soil and plant due to evapotranspiration in the greenhouse environment. The controller design involves a data driven predictive model identified and integrated with the MPC designer in MATLAB and thereafter exported in Simulink for simulation. The generate controller code was modified and deployed on a Raspberry Pi 4 controller to generate a pulse width modulated signal to drive the pump for the control water mixed with fertilizer. To achieve enhancement of controller an Internet of Things (IoT) integration was used for easy soil, weather, and plant monitoring which are used to update the MPC model for the irrigation control. The performance of the proposed MPC controller deployed drip irrigated Greenhouse(GH1) is benchmarked against an existing automatic evapotranspiration (ETo) model based controller in Greenhouse(GH2), with each greenhouse containing 80 poly bags of Cantaloupe plant with similar growth stage. The results obtained shows that, the proposed MPC-based irrigation system has higher water productivity index of 36.8 g/liters, good quality of fruit with average sweetness level of 13.5 Brix compared to automatic ETo-based irrigation system with 25.6 g/liters and 10.5 Brix respectively. However, the total mass of harvested fruit for ETo-based irrigation system is higher than MPC-based irrigation system by 21.7%. The performance of the proposed MPC controller was achieved through the integration of event based scheduling with IoT monitoring as well as inclusion of evapotranspiration effect in the plant dynamics.
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DOI: 10.1016/j.atech.2023.100179
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