In large-scale modern greenhouses, automation of the greenhouse environment using basic timer-based actuators or traditional control algorithms that require offline sensor feedback for switching devices is inefficient. Because of their implementation flexibility, contribution to energy conservation, and yield predictability, wireless instruments that are integrated with artificial intelligence algorithms and knowledge-based decision support systems have piqued growers’ interest.
The proper integration of existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems is required for sustainable production of fruits and vegetables in greenhouse environments with reduced energy inputs. This chapter gives an overview of a greenhouse automation workflow using distributed wireless nodes that have been custom-designed based on the strong LoRa WAN modulation.

In large-scale modern greenhouses, automation of the greenhouse environment using basic timer-based actuators or traditional control algorithms that require offline sensor feedback for switching devices is inefficient. Because of their implementation flexibility, contribution to energy conservation, and yield predictability, wireless instruments that are integrated with artificial intelligence algorithms and knowledge-based decision support systems have piqued growers’ interest.

The proper integration of existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems is required for sustainable production of fruits and vegetables in greenhouse environments with reduced energy inputs. This chapter gives an overview of a greenhouse automation workflow using distributed wireless nodes that have been custom-designed based on the strong LoRa WAN modulation.

To demonstrate connection stability, robustness, and dependability, sample findings from commercial and research greenhouse experiments with IoT hardware and software have been supplied. The approach shown here allows AI to be deployed on embedded hardware such as CPUs and GPUs, as well as cloud-based streaming systems that collect exact measurements from several sensors in various locations throughout greenhouse environments.

Microclimate and fertigation control and automation inside greenhouses have helped to improve the sustainability of closed-field agriculture by lowering water, fertiliser, and energy demands while increasing output and profit.

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