# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 5 Conclusions  
In Agriculture 4.0 and beyond, data analysis as a tool for decision support systems will become standard. Advances in sensors and hardware technologies will make collecting data from agrometeorological stations and other sources increasingly affordable. For example, the LAORE agency is currently promoting new initiatives for in-field data acquisition using smartphone technology, which is widely accessible to producers. This will enable the anticipation of crop dynamics, including the risk of diseases and pest occurrence, and pave the way for more sustainable, economical, and environmentally friendly agriculture. Platforms like SMARTerra, which integrate sensor networks, software systems, and advanced mathematical tools for data analysis, will likely become more popular among end-users, including farmers, agronomists, and technicians from local agricultural agencies. This convergence of leading-edge ICT technologies facilitates a shift from the traditional district-based approach to a new paradigm where analysis at the single crop scale becomes possible but also poses some problems, including the following:  
* The risk to exaggerate data from individual stations (outliers problem);
* The need for improved tools for the interpolation of irregularly distributed data;
* The potential exposure to ill-conditioned mathematical models;
* Data confidentiality concerns as the high-resolution approach can reveal information at the individual farm scale, posing a risk of market disturbance. Some of these issues are addressed in a forthcoming paper [7]. The last point requires careful consideration on a case-by-case basis and close interaction and feedback from end-users, including experts in farm business planning and economics. Several promising future developments can be anticipated, starting with a more systematic strategy that integrates simple deterministic algorithms with advanced, data-driven models, typically based on machine learning. This technology is widely recognized for its ability to combine different types of data and includes effective regularization tools and other valuable numerical resources. This would also allow for the integration of remote imagery from public or commercial satellites to study the interactions between soil properties and the complex ecosystem, including plants, insects, animals, and disease agents [29]. Beyond improvements in data analysis and mathematical methods, substantial advancements are also expected from the agronomic side. For example, our model for predicting the spread of rice blast does not consider the effects of the disease on different rice cultivars. This is a significant point [24] that also pertains to the observation phase, which currently treats susceptible and resistant cultivars the same. This distinction can be incorporated into the risk model through careful parameter calibration, an approach that will become feasible with the expected increase in the dataset. As previously mentioned, both the back-end and front-end of SMARTerra are designed to easily accommodate additional risk models for agriculture based on agrometeorological data, including those available from the ARPAS network that are not currently used (e.g., solar radiation or wind data), thus potentially addressing a wide range of pests and diseases. It also includes crop emergencies that were not considered important in the past but have become increasingly relevant due to changing climate conditions and external factors. Conceptualization, all authors; data curation, M.F. and G.F.; investigation, all authors; methodology, all authors; resources, M.S.G. ; software, F.M., C.M. and A.P. ; supervision, F.M. ; validation, all authors; visualization, C.M. and A.P. ; writing--original draft, F.M. and A.P. ; writing--review and editing, F.M. and A.P. All authors have read and agreed to the published version of the manuscript. The activity at CRS4 was funded by Regione Autonoma della Sardegna under project \"AI\".