## 1 Introduction
These systems have various variants that fulfill different approaches, such as expert-based knowledge systems like Mamdani [30; 31; 32]; Takagi-Sugeno systems [33] which combine fuzzy rules with linear equations; and hybrid models like Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which combine neural networks with fuzzy logic [34]. Each variant offers unique advantages and applications in different domains. After the aforementioned information, it can be established that stevia is an important plant and product worldwide, and during its cultivation process, many variables affect stevia's biomass and stevisoside production, such as soil variables. In order to supervise these parameters, a monitoring system can be implemented, and many solutions provide a feasible option using a WSN and a cloud server as main components. To provide a concise answer for the SQ, a data processing technique such as fuzzy logic model can be implemented, which leads to the hypothesis that implementing a Mamdani fuzzy logic model that uses the pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium of the stevia crop soil as inputs, gathered from a monitoring system, can determine the SQ with a statistically significant determination coefficient (\(r^{2}\)). By obtaining insights into the SQ of the stevia crop, producers can make informed decisions and implement necessary measures to enhance stevia crop production. Nevertheless, another crucial factor to consider is the adoption of technology, a topic addressed in [35]. Several barriers to technology adoption exist, potentially preventing farmers or stakeholders from integrating technological innovations into their processes. These barriers might encompass social or cultural norms, the migration costs of technology, insufficient dissemination or diffusion, limited operational or technical knowledge, and a need for continuous education about emerging technologies. Using these barriers as a foundation, various strategies can be proposed to overcome them and consequently boost technology adoption. These strategies may be focused on enhancing farmers' understanding through effective communication about the application of agronomic innovations and their potential impacts on production quality. Another approach could involve providing demonstrations or samples showcasing the capabilities of the innovation [36]. In this study, as emphasized by [37], it is important to disseminate the proposed monitoring system to farmers through effective communication. This includes conveyingthe range of variables it can measure, enhancing their understanding of their stevia crop soil, demonstrating the user-friendly nature of the web platform for data access, highlighting the straightforward and swift maintenance process, discussing the potential for expanding measurements to other variables if needed and emphasizing the benefits of informed decisions for irrigation and fertilization. If feasible, a practical demonstration of the system's operation could further enhance the communication process. The contributions of this work can be summarized as follows: (a) the design and implementation of an effective Mamdani fuzzy inference system that accurately determines physicochemical qualities, macronutrient concentration, and overall SQ using seven essential soil parameters of a Stevia Morita II crop; (b) the development of a WSN utilizing ESP32-based boards with embedded LoRa communication for efficient collection of the soil parameters using recommended standard 485(RS-485)-based sensors; and (c) the integration of the WSN with a custom web platform, enabling data storage and processing and providing an intuitive user interface for easy access, visualization, and data export.