Smith-Ballester, Laura CristinaFraile Gil, FranciscoChippendale, PaulCouceiro, MicaelPiccinini, Giacomo2026-01-192026-01-192025-06-19979-8-3315-8534-12693-8855https://riunet.upv.es/handle/10251/231784[EN] The FEROX project explores the integration of Artificial Intelligence (AI), Unmanned Aircraft Systems (UAS), and big data analytics to optimize wild berry harvesting in Nordic forests. Traditional foraging methods are limited by low harvesting efficiency, physical strain on workers, and challenges in locating berry-rich areas. To address these limitations, FEROX develops autonomous drone fleets equipped with LiDAR and RGB cameras for berry detection, predictive yield mapping, and navigation under dense forest canopies. Additionally, heavy-lift drones are deployed to transport harvested berries, reducing manual labour demands. This paper details the AI models, drone coordination strategies, and IoT-based enhancements developed within the project. Field trials demonstrate significant improvements in yield estimation accuracy, navigation efficiency, and worker productivity, contributing to the advancement of AIdriven precision forestry and sustainable foraging practices.Reserva de todos los derechosAIUASForestryWild berry harvestingLiDARComunicación en congreso10.1109/ICE/ITMC65658.2025.11106650Abierto