## 4 Discussion  
The results of this study demonstrate that the developed monitoring system effectively measures and analyzes key soil parameters, such as pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium, to assess SQ in stevia crops. The findings align with our initial hypotheses, indicating that the model can determine SQ effectively by using the seven aforementioned soil parameters because the obtained \(r^{2}\) was 0.906 for measured data, and a value of 0.532 was obtained for randomly generated data, surpassing the threshold of 0.5 when compared to a linear model, indicating in both cases a statistically significant result. Furthermore, the observed relationships between the measured parameters and the fuzzy model outputs provide valuable insights into the physicochemical quality and macronutrient concentration of the soil. Once the monitoring system has been tested in the field, is accepted by farmers, and is statistically significant, it is ready for replication for Stevia Morita II crops and other types of crops, under the consideration that some range adjustments for variables and even rules have to be made.  
No other work was found that also focused on assessing soil quality for a Stevia Morita II crop using a Mamdani fuzzy model; however, in the literature, different monitoring systems are presented, which, like this one, use wireless sensor networks to measure, transport, and store data. LoRa and WIFI protocols were common choices as well. Other works also implement fuzzy models. These are valid for their intended purpose such as predicting the chance of the propagation of plant diseases or assessing students' performance.  
The implications of these findings are significant for stevia producers. This innovation was effectively introduced in a Mexican stevia plantation, made possible by employing strategies to overcome adoption barriers. Effective communication played a vital role in  
Figure 15: Histograms of the randomly generated data for fuzzy model validation.  
demonstrating how the system addressed the technical requirement for assessing the crop's soil quality. The system's user-friendly and visually engaging interface also enhanced its utility for farmers. The monitoring system provides periodic data and historical access, enabling informed decisions about soil management and crop health. Monitoring SQ indicators allows for optimizing nutrient application, irrigation strategies, and overall crop productivity. Furthermore, the system's continuous operation, coupled with a reliable web server, ensures uninterrupted data collection and analysis, enhancing its practicality and usefulness in the field.  
Based on the findings of this study, several promising avenues for future research emerge. Expanding the monitoring system to other crop systems would provide valuable insights into the generalizability of the developed approach. Additionally, incorporating additional parameters and variables, such as organic matter content or microbial activity, could enhance the comprehensiveness and accuracy of the SQ assessment. Furthermore, exploring alternative FISs, such as Takagi-Sugeno-Kang or other artificial intelligence techniques, would be beneficial to further improve the precision and predictive capabilities of the monitoring system.