Mini plant factory development using IoT and cloud system for urban greens cultivation

Abstract A significant urban population increases with the rising demand for higher food quality and security. Adapting the current food chain system and our eating patterns are needed to make them more sustainable. Plant factories are one approach for making the food chain system more sustainable....

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2022-12, Vol.1116 (1), p.12028
Main Authors: Dzaky, M A F, Nugroho, A P, Prasetyatama, Y D, Falah, M A F, Sutiarso, L, Okayasu, T
Format: Article
Language:English
Subjects:
Actuation
Actuators
Calibration
Control systems
Crop production
Cultivation
Data logging
Data retrieval
Factories
Food chains
Food quality
Horticultural crops
Industrial plants
Microclimate
Monitoring
Root-mean-square errors
Urban areas
Urban populations
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Summary:Abstract A significant urban population increases with the rising demand for higher food quality and security. Adapting the current food chain system and our eating patterns are needed to make them more sustainable. Plant factories are one approach for making the food chain system more sustainable. However, plant factory systems typically require significant initial capital expenditure. As a result, a mini plant factory, which is more affordable and flexible, will be appropriate in urban areas. The objective of this study was to design, build, and evaluate a mini plant factory system for horticultural crop production. The mini plant factory system comprises a mini cultivation room with three growing shelves equipped with a microclimate sensor, nutrient dosing system, artificial growth light, and ventilation system. Sensors and actuators are integrated with the Agrieye Cloud system for monitoring and control and can autonomously execute data retrieval and actuation. Mini Plant Factory operates by automatically capturing monitoring data every five minutes; if a value exceeds the limit, the actuator control system activates to stabilize that value. The linear regression test yielded the most excellent R 2 value of 0.999 for sensor calibration. The validation test was conducted using RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error). The results show that the lowest RMSE values are 0.133, and the lowest MAPE values are 0.082%. The data logging system’s performance is perfect. The control and scheduling system works well but does not function optimally since there are no interruptions throughout the data gathering process.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/1116/1/012028