Prototype of a system for monitoring and predictive modeling of soil salinity based on electrical conductivity following inoculation with halophilic bacteria in raspberries (Rubus idaeus L.)

• DOI: https://doi.org/10.22533/at.ed.13176226240212

• Palavras-chave: Precision agriculture; IoT sensors; halotolerant rhizobacteria

• Keywords: Precision agriculture; IoT sensors; halotolerant rhizobacteria

• Abstract:

  Soil salinity limits crop performance in protected environments, where salt accumulation in the root zone can intensify and require continuous monitoring to inform management decisions. This study developed and validated a prototype real-time monitoring station (OFA) with multiparameter data acquisition and remote data transmission to assess soil salinity operationalized through soil electrical conductivity (EC) as an integrated indicator of soluble salts, under a cumulative inoculation scheme with halophilic bacteria (halotolerant rhizobacteria) in raspberries (Rubus idaeus L.) grown in a macro-tunnel in Jocotepec, Jalisco, Mexico (1,473 m a.s.l.). This IoT-based precision agriculture platform enabled the longitudinal acquisition and remote transmission of edaphic data for analysis. The system integrated sensors for EC, pH, total dissolved solids (TDS), salinity, and temperature, with automated database generation and export for analysis in SAS Studio 2025. The analytical layer was structured as a longitudinal series of 25 observations between days 1 and 72, incorporating as covariates the cumulative number of inoculations (INO), days elapsed (DIAS), and the physicochemical variables recorded by the prototype. Pearson’s correlation revealed negative associations between EC and INO (r = −0.6475; p = 0.0005) and DAYS (r = −0.7031; p < 0.0001), as well as positive associations with TDS (r = 0.9431; p < 0.0001) and temperature (r = 0.8753; p < 0.0001). The multiple regression model was significant (F = 34.92; p < 0.0001) and explained 92.09% of the variation in EC (R² = 0.9209; adjusted R² = 0.8945), with TDS as a robust positive predictor (p < 0.0001) and temperature as a partially negative predictor (p = 0.0481). Model diagnostics supported the internal stability of the fit across the experimental range, although with a relatively greater influence of observations at the high end of EC. Overall, the results indicate that the prototype enables traceable monitoring and local predictive modeling of salinity based on soil EC, providing a useful instrumental platform for evaluating biological interventions aimed at managing salinity in raspberries under macro-tunnel conditions.