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Smart Irrigation with Fuzzy Decision Support Systems in Trentino Vineyards
Romeo Silvestri1, Massimo Vecchio1, Miguel Pincheira1
1Fondazione Bruno Kessler, 38122 Trento, Italy.
Sensors (Basel, Switzerland)
|December 11, 2025
Summary
This study compares two fuzzy-logic irrigation systems for vineyards. The Mamdani system uses expert rules, while the Takagi-Sugeno system learns from field data, offering insights into water conservation and crop stress management.
Area of Science:
- Agricultural Engineering
- Environmental Science
- Computer Science
Background:
- Efficient water management is crucial for agriculture due to climate change and water scarcity.
- Vineyard irrigation requires sophisticated decision support systems to optimize water use.
Purpose of the Study:
- To conduct a field-based comparative evaluation of two fuzzy-logic irrigation decision support systems (Mamdani and Takagi-Sugeno) for vineyard management.
- To assess the performance of these systems in terms of water use efficiency, soil moisture control, and robustness.
Main Methods:
- Field-based evaluation of Mamdani-type and Takagi-Sugeno fuzzy-logic controllers.
- Integration of soil moisture sensing, short-term forecasting, and weather predictions.
- Counterfactual simulations and bootstrap-based statistical analysis for performance assessment.
Main Results:
- Both fuzzy-logic systems provide optimized irrigation recommendations.
- Distinct strengths and trade-offs were identified between the Mamdani (expert rules) and Takagi-Sugeno (automated learning) approaches.
- The study revealed varying performance in water conservation versus crop stress mitigation.
Conclusions:
- The comparative evaluation offers practical insights for designing and deploying intelligent irrigation management solutions in agriculture.
- The choice between Mamdani and Takagi-Sugeno systems depends on specific management goals regarding water conservation and crop health.
- Fuzzy-logic controllers show promise for enhancing water use efficiency in vineyards under variable environmental conditions.
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