Vejar-Cortés, A.-P.; García-Díaz, N.; Soriano-Equigua, L.; Ruiz-Tadeo, A.-C.; Álvarez-Flores, J.-L. Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network. Appl. Sci.2023, 13, 9507.
Vejar-Cortés, A.-P.; García-Díaz, N.; Soriano-Equigua, L.; Ruiz-Tadeo, A.-C.; Álvarez-Flores, J.-L. Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network. Appl. Sci. 2023, 13, 9507.
Vejar-Cortés, A.-P.; García-Díaz, N.; Soriano-Equigua, L.; Ruiz-Tadeo, A.-C.; Álvarez-Flores, J.-L. Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network. Appl. Sci.2023, 13, 9507.
Vejar-Cortés, A.-P.; García-Díaz, N.; Soriano-Equigua, L.; Ruiz-Tadeo, A.-C.; Álvarez-Flores, J.-L. Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network. Appl. Sci. 2023, 13, 9507.
Abstract
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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