# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
###### 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. agriculture; agreoreology; decision support system; WebGIS; plant disease; pest prediction; rice blast; locust; geostatistical interpolation; Sardinia +
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While numerous methods exist for the early detection of crop emergencies, based on in-field or remote sensors installed on drones or ground vehicles and governed by artificial intelligence (AI) or computer vision algorithms (see [6] and references therein), the focus here is on the prediction _before_ problems arise. Models for such risk forecasting come in various forms, all sharing a common feature: they heavily rely on the strong interplay between crop behavior as a whole and weather conditions. The latter are well known to affect plant growth and development, trigger pathological outbreaks, and regulate pest spread. Agrometeorological parameters such as temperature, humidity, and rainfall are invariably involved in most agricultural risk models, like those cited above, which have proven to be effective. SMARTerra, the infrastructure we propose, has been designed with a dual purpose: (i) to provide a fully automatic system capable of processing measured and predicted weather data to generate near real-time forecasts of the risk associated with plant pests for the entire Sardinia region (Italy) and (ii) to perform this task at the single-farm scale (10 m). The latter objective is only partially guaranteed by standard interpolation techniques provided in common software libraries and therefore deserves a specific, innovative mathematical approach. This is the subject of a methodological paper that the authors are currently submitting for publication [7]. SMARTerra is designed to provide growers and agricultural professionals with a reliable, accessible tool based on advanced ICT technology. Its web interface allows users to view detailed risk prediction maps, facilitating timely, informed decisions and the implementation of targeted prevention strategies. Currently, the system integrates models for predicting the risk of two specific agricultural threats in Sardinia, detailed below, which can be regarded as working examples. However, SMARTerra is easily adaptable to any geographic area and a range of agricultural emergencies, given an available network of agrometeorological stations and suitable risk models for the relevant crops. Few studies exist in the literature on this topic, mainly referred to as Integrated Pest Management (IPM). Reference [8] reviews several IPMs, either leveraging crop rotations or typically starting from the diagnosis of existing pests, rather than attempting to anticipate their appearance and spread.