Anomaly Identification in a Smart Farming Platform

In recent years, anomaly detection in various situations has emerged as an area of increasing theoretical and practical interest. The article presents an approach for developing a component for identifying and locating possible anomalies in agricultural processes. A possible classification of suspected types of anomalies specific to agriculture is presented. Three groups of methods suitable for detecting anomalies in agriculture are briefly described. The component will be integrated into the smart farming platform known as ZEMELA. ZEMELA is a regional platform where the focus is placed on maximum exploitation of the specifics of the observed agricultural area. To demonstrate the application of our approach, we present a specific example of identifying an anomaly, which in this case is a tomato disease.