# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
###### Abstract
Notably, [9] presents a Decision Support System (DSS) for tree fruit growers based on meteorological data (measured by a single station--the nearest one) and model predictions, accessible via an annual fee. Such systems highlight a common limitation: they rely on non-optimal approximations of measured agrometeorological parameters. On a different ground, data-driven methods hold significant promise for pest prediction. Unlike deterministic models, they do not require the careful calibration of numerous parameters that are crop-specific and dependent on soil features, plant cultivar, general environmental conditions of the local area (including the dynamics of previous pest occurrences), and other factors. This represents a significant advantage. In our view, the main limitation of deep learning analysis is the need for large, annotated training datasets. While long-term time series of agrometeorological data are often available (as they are in our case of interest), a corresponding systematic, quantitative characterization of a particular pest or disease for the considered time range is seldom available. Therefore, whenever a new model for a particular emergency is desired, an on-field data recording of the dynamics of such emergency (the annotation for training) is necessary. This step is usually more demanding and time-consuming than the calibration of deterministic algorithms. Systematic and valuable reviews of different modeling methods, both deterministic and data-driven, can be found in [10; 11; 12], where the authors propose a holistic approach for linking crop, insects, and environmental conditions, and in [13] for plant disease modeling as part of a comprehensive approach to develop decision-making tools for IPM. To illustrate the workflow and outcomes of the web-based method we propose, we consider two case studies related to plant pathologies and crop-feeding insects particularly invasive in Sardinia: the rice blast (_Pyricularia oryzae_) disease and the infestation of Moroccan locust (_Dociostaurus maroccanus_). SMARTerra is currently accessible to a selected group of end-users for testing. It will soon be available to farmers, agricultural agencies, and industry professionals once the terms of service, which are currently being finalized, are released. An online demo is available upon request to showcase the system's capabilities. A precursor to SMARTerra exists, where weather data--both measured from a network of stations and predicted by a local meteorological model (BOLAM)--are manually processed to evaluate risk indices for a limited set of locations and at low resolution. This process employs spreadsheet-based technology, which is time-consuming and requires the daily involvement of several human experts susceptible to errors, such as the incorrect import or processing of data (e.g., location or date swapping). The detection of outliers in weather measurements is not systematically performed at the current development stage of SMARTerra: the implementation of this functionality will be described in [7]. The outcomes of SMARTerra allow for the timely notification of affected users not only through an online publication but also through automated and/or personalized bulletin delivery via email, smartphone applications, and other instant messaging applications. The data, methodologies, infrastructure of our decision support system, and the geographical areas of the study are illustrated in Section 2. Examples of weather and risk maps for rice blast disease and locust egg hatching, which are outputs of SMARTerra, are shown in Section 3. A discussion on the improvements, innovations, and benefits delivered by SMARTerra is provided in Section 4. Finally, Section 5 presents the conclusions along with future development scenarios.