PesTwin: a biology-informed Digital Twin for enabling precision farming

2026-03-11

AI Summary

This study addresses the threat posed by invasive pests to global food security by proposing a novel framework that integrates biological knowledge with digital twin technology to simulate pest invasion dynamics. By fusing laboratory-derived biological data, meteorological information, and geospatial GIS data from agricultural fields, the authors develop an agent-based model of ecological interactions to enable high-precision spatiotemporal prediction of spotted-wing drosophila (Drosophila suzukii) infestations in real-world farming environments. This approach represents the first integration of multi-source heterogeneous data with rule-driven simulation, offering a decision-support tool for precision agriculture that enables fine-grained pest management strategies. The framework significantly enhances the efficacy of pest control interventions and contributes to improved agricultural productivity.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

Abstract

In a context of growing agricultural demand and new challenges related to food security and accessibility, boosting agricultural productivity is more important than ever. Reducing the damage caused by invasive insect species is a crucial lever to achieve this objective. In support of these challenges, and in line with the principles of precision agriculture and Integrated Pest Management (IPM), an innovative simulation framework is presented, aiming to become the digital twin of a pest invasion. Through a flexible rule-based approach of the Agent-Based Modeling (ABM) paradigm, the framework supports the fine-tuning of the main ecological interactions of the pest with its crop host and the environment. Forecasting insect infestation in realistic scenarios, considering both spatial and temporal dimensions, is made possible by integrating heterogeneous data sources: pest biodata collected in the laboratory, environmental data from weather stations, and GIS data of a real crop field. In this study, an application to the global pest of soft fruit, the invasive fruit fly Drosophila suzukii, also known as Spotted Wing Drosophila (SWD), is presented.

Problem

• invasive insect species
• agricultural productivity
• precision farming
• pest infestation
• food security

Innovation

• Digital Twin
• Agent-Based Modeling
• Precision Agriculture
• Integrated Pest Management
• Drosophila suzukii