Indoor agriculture is emerging as a promising approach for increasing the efficiency and sustainability of agri-food production processes. It is currently evolving from a small-scale horticultural practice to a large-scale industry as a response to the increasing demand. This led to the appearance of plant factories where agri-food production is automated and continuous, and the plant environment is fully controlled.
While plant factories improve the productivity and sustainability of the process, they suffer from high energy consumption and the difficulty of providing the ideal environment for plants. As a small step to address these limitations, in this article, we propose to use internet of things (IoT) technologies and automatic control algorithms to construct an energy-efficient remote control architecture for grow light monitoring in indoor farming.
The proposed architecture consists of using a principal–agent device configuration in which the agent devices are used to control the local light conditions in growth chambers while the principal device is used to monitor the plant factory through wireless communication with the agent devices. The devices all together make a 6LoWPAN network in which the RPL protocol is used to manage data transfer. This allows for the precise and centralized control of the growth conditions and the real-time monitoring of plants. The proposed control architecture can be associated with a decision support system to improve yields and quality at low costs. The developed method is evaluated in emulation software (Contiki-NG v4.7), its scalability in the case of large-scale production facilities is tested, and the obtained results are presented and discussed. The proposed approach is promising in dealing with control, cost, and scalability issues and can contribute to making smart indoor agriculture more effective and sustainable.
Hadj Abdelkader, O.; Bouzebiba, H.; Pena, D.; Aguiar, A.P. Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture. Sensors 2023, 23, 7670. https://doi.org/10.3390/s23187670