Résumé
Applying fertilizers and pesticides near bodies of water poses significant environmental risks, primarily due to the potential for chemical runoff to contaminate aquatic ecosystems. To avoid this, regulations establish prohibition zones based on environmental and application-specific parameters, such as terrain slope, wind speed, precipitation, and the type and composition of substances used. This paper presents an autonomous robotic system that was developed to comply with these regulations while maximizing usable agricultural land. The robot scans its environment with sensors, including LiDAR, to measure features such as the distance to nearby bodies of water and the slope of the ground underneath. The InteGraal reasoning framework uses samples of regulations encoded in machine-readable RDF formats (using PAM vocabularies) and sensor observations modeled by the Semantic Sensor Network Ontology (SSNO) to make real-time decisions about where to stop or resume spraying. We extend existing vocabularies to include fertilizer-specific regulations, ensuring a comprehensive, semantically rich decision-support system for autonomous farms.