Fuzzy-IoT smart irrigation system for precision scheduling and monitoring

Adopting and developing sustainable precision irrigation systems allows for improving productivity, reducing water and energy losses in agricultural fields. This paper deals with the development and real-time implementation of a smart precision irrigation system (SPIS). The proposed system combines two irrigation approaches, processed by a feedback fuzzy logic (FL) controller, and long-range data transmission and monitoring via the LoRa protocol. The combined approaches not only enhance overall performance but also address several limitations commonly associated with existing approaches. The proposed system uses the collected data from the sensors unit to provide the optimal irrigation decision and communicates it with the operator (farmer) via a developed IoT platform. Data from soil moisture, ambient temperature, solar irradiance, and rainfall sensors are processed through the FL controller to adjust the irrigation times. The inputs and outputs are fuzzified by trapezoidal and triangular membership functions. Mamdani's fuzzy inference is used to control the system through a set of linguistic control rules for a more accurate decision for each scenario. The field experimentation of the developed SPIS in the eastern region of Morocco shows high efficiency and reliability by saving water and energy compared to traditional irrigation control. The proposed system, with remote data monitoring, promotes better management of irrigation, water, and energy consumption, and it is suitable to be mounted with any type of irrigation installation in the field, without extensive modifications.

Automated summary

This paper introduces a smart precision irrigation system that utilizes a feedback fuzzy logic controller and the LoRa protocol for remote data transmission and monitoring. The system adjusts irrigation times based on data collected from sensors and has shown high efficiency and reliability in field experimentation.