publications

2026

1. NCAA (submitted)

   

   A Retrieval-Augmented LLM Framework for PRISMA-Aligned Systematic Reviews

   Romeo Silvestri, Miguel Pincheira, Massimo Vecchio, and 1 more author

   2026

   Preprint available at ResearchGate and SSRN. Currently under review in Neural Computing and Applications (NCAA).

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   Systematic reviews and meta-analyses are essential for advancing scientific research, but they are time-consuming, labor-intensive, and prone to human error during literature selection and data extraction. This paper proposes a framework that integrates Large Language Models (LLMs) and retrieval-augmented generation (RAG) into the PRISMA 2020 workflow to assist and standardize key review tasks. The framework uses structured query templates to ensure reproducible bibliographic searches, an LLM-driven pipeline for title and abstract screening, and a RAG-based full-text eligibility assessment. Both automated stages rely on structured prompt templates that encode inclusion and exclusion criteria as machine-interpretable instructions. Beyond selecting relevant papers, it extracts specific information from documents, such as application details or datasets, and harmonizes it into a standardized metadata schema. To evaluate our proposal, we performed an experimental validation using an existing systematic literature review on Human Action Quality Assessment (AQA) as a reference. The framework achieved an extraction accuracy of 85.11% for domain-specific datasets and identified 25 additional data sources not reported in the original review. The article detection rate of 72.34% was primarily attributable to the dataset-oriented query design adopted in the identification phase, while the LLM-based screening and RAG-based eligibility stages retained 97.14% of the reference studies that entered the automated pipeline. These results suggest that the framework can effectively assist in identifying relevant studies and extracting information while maintaining alignment with PRISMA 2020 guidelines. The proposed approach is structured to facilitate adaptation to other research areas by declaring new query and promtp templates.

   @misc{silvestri2026rag,
     title = {A Retrieval-Augmented LLM Framework for PRISMA-Aligned Systematic Reviews},
     author = {Silvestri, Romeo and Pincheira, Miguel and Vecchio, Massimo and Antonelli, Fabio},
     year = {2026},
     note = {Preprint available at ResearchGate and SSRN. Currently under review in Neural Computing and Applications (NCAA).},
   }

2. IEEE MetroInd

   

   Deep Reinforcement Learning for Irrigation Optimization Based on Crop-Soil Dynamics

   Romeo Silvestri, Mattia Antonini, Massimo Vecchio, and 1 more author

   2026

   Paper accepted for publication and soon to appear online.

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   Irrigation management is a key challenge in modern agriculture due to water scarcity and increasing climate variability. This work proposes a deep reinforcement learning (DRL) framework for irrigation scheduling that addresses data scarcity by training the agent within a digital twin of crop–soil dynamics. The environment combines a KNN-based weather generator that extends a 30-year historical record into 1000 synthetic seasons, an XGBoost model for daily soil water tension estimation, and the AquaCrop simulator for crop biomass modeling. A DRL agent is trained using Proximal Policy Optimization to learn a weather-aware irrigation policy without predefined rules. The framework is evaluated on vineyard field data from the Val d’Adige region (Trentino, Italy) over the 2023–2024 growing seasons. Results show a 19% reduction in seasonal water use and more than a twofold increase in the number of days within the optimal soil tension range, while maintaining comparable crop productivity to observed practices.

   @unpublished{silvestri2026rl,
     title = {Deep Reinforcement Learning for Irrigation Optimization Based on Crop-Soil Dynamics},
     author = {Silvestri, Romeo and Antonini, Mattia and Vecchio, Massimo and Antonelli, Fabio},
     year = {2026},
     note = {Paper accepted for publication and soon to appear online.},
   }

2025

1. MDPI Sensors

   

   Smart Irrigation with Fuzzy Decision Support Systems in Trentino Vineyards

   Romeo Silvestri, Massimo Vecchio, Miguel Pincheira, and 1 more author

   Sensors, 2025

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   Efficient water management is a critical challenge for modern agriculture, particularly in the context of increasing climate variability and limited freshwater resources. This study presents a comparative field-based evaluation of two fuzzy-logic-based irrigation decision support systems for vineyard management: a Mamdani-type controller with expert-defined rules and a Takagi–Sugeno system designed to enable automated learning from ultra-local historical field data. Both systems integrate soil moisture sensing, short-term forecasting, and weather predictions to provide optimized irrigation recommendations. The evaluation combines counterfactual simulations with a bootstrap-based statistical analysis to assess water use efficiency, soil moisture control, and robustness to environmental variability. The comparison highlights distinct strengths of the two approaches, revealing trade-offs between water conservation and crop stress mitigation, and offering practical insights for the design and deployment of intelligent irrigation management solutions.

   @article{silvestri2025smart,
     title = {Smart Irrigation with Fuzzy Decision Support Systems in Trentino Vineyards},
     author = {Silvestri, Romeo and Vecchio, Massimo and Pincheira, Miguel and Antonelli, Fabio},
     journal = {Sensors},
     volume = {25},
     number = {23},
     pages = {7188},
     year = {2025},
     publisher = {MDPI},
   }

2. IEEE CoDIT

   

   A Fuzzy Decision Support System to Optimize Irrigation Practices in Trentino Region

   Romeo Silvestri, Massimo Vecchio, and Fabio Antonelli

   In 2025 11th International Conference on Control, Decision and Information Technologies (CoDIT), 2025

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   This paper presents the development and evaluation of a Fuzzy Decision Support System for irrigation management to promote sustainable water use in precision agriculture. A Mamdani-type fuzzy logic model was designed to optimize irrigation scheduling for vineyards in the Val d’Adige region of Trentino, Italy. The system integrates expert knowledge with real-time data from tensiometers and weather stations to generate adaptive, site-specific recommendations. Bayesian optimization was used to fine-tune the membership functions of fuzzy variables, enhancing system performance. Field evaluations conducted in 2023 across multiple sectors assessed total water use, average soil moisture, and days exceeding critical moisture thresholds. Results show that the system reduced total water consumption by over 52% compared to traditional methods while maintaining soil moisture within optimal levels. These findings underscore the potential of combining fuzzy logic and IoT-based sensing to support scalable, adaptive irrigation strategies across various crops and regions.

   @inproceedings{codit25,
     author = {Silvestri, Romeo and Vecchio, Massimo and Antonelli, Fabio},
     booktitle = {2025 11th International Conference on Control, Decision and Information Technologies (CoDIT)},
     title = {A Fuzzy Decision Support System to Optimize Irrigation Practices in Trentino Region},
     pages = {1-6},
     volume = {1},
     year = {2025},
   }

2024

1. IEEE MetroAgriFor

   

   Comparative Analysis of Soil Moisture Interpolation Techniques in Apple Orchards of Trentino Region

   Romeo Silvestri, Massimo Vecchio, Miguel Pincheira, and 1 more author

   In 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 2024

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   This paper provides valuable insights into the application of spatial interpolation techniques in smart agriculture and highlights the potential for further improvements through the integration of advanced geostatistical models. Specifically, it evaluates and compares two spatial interpolation techniques, Inverse Distance Weighting and Ordinary Kriging, for estimating soil moisture in apple orchards located in the Val di Non region of Trentino, Italy. Data were gathered from 18 tensiometer sensors deployed across the apple orchards, providing continuous soil moisture measurements over a specified time frame in 2023. The accuracy of both interpolation methods was assessed using root mean square error as the primary evaluation metric, with various validation methods employed to ensure robustness. Additionally, statistical analyses were conducted to determine the significance of differences in performance between the methods. The results indicate that Inverse Distance Weighting, despite its computational efficiency, slightly outperforms Ordinary Kriging in terms of accuracy, with statistically significant lower error values, making it a preferable choice for real-time soil moisture mapping and precision irrigation management in the region.

   @inproceedings{silvestri2024comparative,
     title = {Comparative Analysis of Soil Moisture Interpolation Techniques in Apple Orchards of Trentino Region},
     author = {Silvestri, Romeo and Vecchio, Massimo and Pincheira, Miguel and Antonelli, Fabio},
     booktitle = {2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
     pages = {557--562},
     year = {2024},
     organization = {IEEE},
   }