## 1 Introduction
Monitoring systems, often referred to as internet of things (IoT) or precision agriculture systems, have emerged as a common solution for collecting data on various variables of interest. These systems enable the gathering of environmental variables [13; 14; 15; 16; 17], soil parameters [18; 19; 20; 21; 22], and water parameters [23]. Typically, such systems comprise electronic sensors designed to measure specific variables, along with microcontrollers or interconnected microcontrollers that interpret the sensor data and transmit it wirelessly as a wireless sensor network (WSN). In the case of a networked system, a dedicated device receives the transmitted data and stores them in a database, such as structured query language (SQL), either by sending requests to a cloud server or utilizing IoT services like ThingSpeak [15; 18]. To facilitate data analysis, these monitoring systems often include a graphical interface that allows users to access and visualize the collected data. Moreover, the interface may support data export for further processing and analysis [13; 14; 16; 17; 19; 20; 21; 22; 23]. When implementing a monitoring system, selecting the appropriate technologies and tools is crucial. Among the commonly used microcontrollers, Espressif Systems Processor 32 (ESP32) and 8266 (ESP8266) are popular choices. These microcontrollers are often integrated into embedded development boards such as node microcontroller units (NodeMCUs) and TTGO boards made by LILYGO. Their popularity stems from their user-friendly nature and support for the Institute of Electrical and Electronics Engineers (IEEE) 802.11 protocol, also known as WIFI, enabling wireless communication and internet access when connected to compatible networks [14; 15; 17; 18; 22]. Wireless data transmission in monitoring systems can be achieved through various protocols, including radio frequency modules, long-range radio (LoRa), and IEEE 802.11. Each protocol offers distinct advantages, such as range, data rate, bandwidth, and module cost [22; 24]. In the literature, LoRa and IEEE 802.11 have been frequently mentioned and used. LoRa is known for its long-distance transmission capability, although at a lower data rate compared to WIFI. It also has lower power consumption during its operation, which is particularly beneficial for WSNs that rely on battery power [19; 24]. Data collected from monitoring systems can be stored in various ways. One common option is to send measurements over the internet to an IoT platform, such as ThingSpeak, Adafruit input-output (IO), clouds, or the IBM Watson IoT Platform [15; 17; 18]. These platforms provide convenient and scalable solutions for storing and managing sensor data. Another approach is to implement a custom cloud server. This can be achieved by setting up a message queuing telemetry transport (MQTT) broker, which allows for efficient and reliable data transfer between devices and the server. Alternatively, data can be received through hypertext transfer protocol (HTTP) requests, processed, and then stored in a database [16; 20]. Implementing a custom cloud server provides more flexibility and control over the data storage and management process. In some monitoring systems, data processing techniques such as artificial intelligence (AI) models, including neural networks and fuzzy systems, are employed to create valuable insights about the observed variables. These AI models analyze the collected data and generate results that provide a more comprehensive understanding of the variable status and behavior. By using AI-interpreted data, monitoring systems can offer a more accessible and user-friendly approach to gaining meaningful insights and making informed decisions [25; 26; 27; 28]. Fuzzy logic systems have been a useful tool since their creation in 1988 [29].