Utilizzo dei dati di gestione delle macchine per migliorare le operazioni forestali sostenibili: modellare l'interazione macchina forestale-suolo

Increasing attention to sustainable forest management has emphasized the need to minimize the environmental impact of mechanized forestry operations. This thesis explores soil-machine interactions in forestry operations, specifically examining how data mining techniques can be leveraged to identify trends and patterns using the FMS database in conjunction with topographic and climatic conditions. These factors include terrain morphology, soil type, wet soil conditions, and weather parameters derived from open-source portals. Moreover, it also examined the interaction between off-road forestry vehicles and various terrain conditions to evaluate machine performance, particularly the rolling resistance coefficient (μr). Since µr coefficient primarily describes machine rolling resistance due to the deformation of the terrain, we consider that the higher µr is, the greater the deformation of the ground and the worse the terrain trafficability, impacting overall performance. This is done by evaluating real-time operational parameters such as engine power, engine revolutions (RPM) and vehicle speed collected via CAN-bus systems adhering to the J1939 standard. Results indicate that integrating these diverse data sources enhances the understanding of machine efficiency and provides practical recommendations for sustainable forest management.