Optimizing Snail Farming: Predictive Modelling of Terrestrial Snail Output Using Machine Learning

Authors

  • Konstantinos Apostolou *

    Department of Ichthyology and Aquatic Environment, University of Thessaly, 38446 Volos, Greece
  • Marianthi Hatziioannou

    Department of Ichthyology and Aquatic Environment, University of Thessaly, 38446 Volos, Greece
  • Dimitris Klaoudatos

    Department of Ichthyology and Aquatic Environment, University of Thessaly, 38446 Volos, Greece

DOI:

https://doi.org/10.55121/nc.v5i1.643

Keywords:

Heliciculture , Cornu aspersum maximum , Machine Learning, Predictive Modeling, Snail Farming, Production Optimization

Abstract

Heliciculture, the cultivation of edible land snails, is gaining prominence as a sustainable and economically viable agricultural sector. Despite this growth, productivity in snail farming remains inconsistent due to variable environmental conditions and empirical management practices. This study applies machine learning (ML) techniques for the first time to predict production outcomes in Cornu aspersum maximum farms across Greece. Data were collected from 30 operational farms using structured questionnaires, covering biological, environmental, and management-related variables. A suite of supervised ML algorithms, Support Vector Machine (SVM), Neural Network, Stochastic Gradient Descent, and Linear Regression, were trained and evaluated using stratified 5-fold cross-validation. SVM was the most accurate model, achieving 85% predictive accuracy. SHAP (SHapley Additive exPlanations) analysis identified snail
density, feed quantity, and mortality rate as the three most critical variables influencing production output. Additionally, hierarchical clustering distinguished two farm clusters with distinct performance patterns, suggesting divergent production strategies. These results demonstrate the potential of ML to inform precision management in heliciculture. By identifying key predictors of productivity, this study provides a decision-support framework for optimizing farm inputs and enhancing operational sustainability. Results highlight the value of digital tools in transforming traditional snail farming into a data-driven and efficient system.

Author Biography

Marianthi Hatziioannou, <p><em>Department of Ichthyology and Aquatic Environment, University of Thessaly, 38446 Volos, Greece</em></p>

Associate Professor
Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly

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Apostolou, K., Hatziioannou, M., & Klaoudatos, D. (2025). Optimizing Snail Farming: Predictive Modelling of Terrestrial Snail Output Using Machine Learning. New Countryside, 5(1), 69–88. https://doi.org/10.55121/nc.v5i1.643

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Vol. 5 No. 1 (January 2026): In Progress

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Copyright (c) 2026 Konstantinos Apostolou, Marianthi Hatziioannou, Dimitris Klaoudatos

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