SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases

Author:

Fiori Michele1ORCID,Fois Giuliano1,Gerardi Marco Secondo2ORCID,Maggio Fabio3ORCID,Milesi Carlo3ORCID,Pinna Andrea3ORCID

Affiliation:

1. ARPAS, Regional Environment Protection Agency of Sardinia, Via Contivecchi 7, 09122 Cagliari, Italy

2. LAORE Sardegna, Regional Agency for Agriculture Development, Via Caprera 8, 09123 Cagliari, Italy

3. CRS4, Center for Advanced Studies, Research and Development in Sardinia, Località Piscina Manna Edificio 1, 09050 Pula, Italy

Abstract

The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis.

Funder

Regione Autonoma della Sardegna

Piano Nazionale Ripresa e Resilienza

Bando a cascata AGRITECH

Legge Regionale

Publisher

MDPI AG

Reference29 articles.

1. European Space Agency (2024, June 30). Pest Prediction. Available online: https://sdg.esa.int/activity/pest-prediction-4807.

2. Oregon State University (2024, June 30). Pest Monitoring & Predictive Tools for Informed Decisions. Available online: https://agsci.oregonstate.edu/oipmc/ipm-tools-professionals/pest-monitoring-and-prediction.

3. U.S. Department of Agriculture (USDA) (2024, June 30). Spatial Analytic Framework for Advanced Risk Information Systems (SAFARIS), Available online: https://safaris.cipm.info/safarispestmodel/StartupServlet?safarishome.

4. Agriculture and Horticulture Development Board (2024, June 30). CP 127 Compendium of Pest Forecasting Models. Available online: https://horticulture.ahdb.org.uk/cp-127-compendium-of-pest-forecasting-models.

5. The Agricultural Model Intercomparison and Improvement Project (AgMIP) (2024, June 30). Pests and Diseases (PeDiMIP). Available online: https://agmip.org/pests-and-diseases/.

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