Stevia rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The optimization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium. The system employs a wireless sensor network to collect quasi-real-time data, which are transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a linear regression model, the system demonstrated acceptable performance, with a determination coefficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops.

Stevia Rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The op-timization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, ni-trogen, phosphorus, and potassium. The system employs a wireless sensor network to collect qua-si-real-time data, which is transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide to farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a line-ar-regression model, the system demonstrated acceptable performance, with a determination coef-ficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops.

Adequate soil quality is essential for ensuring the productivity and sustainability of agriculture, with soil pH being a key variable due to its influence on nutrient availability, microbial activity, and plant development. This study proposes a Mamdani fuzzy inference system (FIS) optimized through metaheuristic algorithms for soil pH prediction in papaya (Carica papaya L.) cultivation, a crop highly sensitive to pH fluctuations within the rhizosphere. Soil temperature and soil moisture were used as independent variables, while the estimated soil pH constituted the dependent variable of the system. Three optimization techniques—genetic algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—were evaluated to optimize the membership functions and fuzzy rule base of the Mamdani FIS. Model performance was assessed through 30 independent runs using 1500 records collected from a commercial papaya plantation. Across the 30 independent runs, the GA-optimized model achieved the best overall predictive performance, with a Mean Absolute Error (MAE) of 0.3636 ± 0.0035, Mean Relative Error (MRE) of 0.0554 ± 0.0004, and mean coefficient of determination (r²) of 0.8699 ± 0.0048. The best observed GA values were an MAE of 0.3305, MRE of 0.0513, and r² of 0.8957. In addition, a web-based decision support platform and an automated Telegram alert system were developed for event-driven pH alert notification. The results confirm that GA-optimized fuzzy systems constitute an effective, interpretable, and practical tool for pH-based soil condition monitoring and agronomic decision support in precision agriculture.