Fostering and Enabling AI, Data and Robotics Technologies for Supporting Human Workers in Harvesting Wild Food

Digital, Industry & SpaceHORIZON-IAID: 101070440
EC Contribution
€27,470
Consortium Size
10 orgs
Start Year
2022
Summary

Wild berries and mushrooms are considered to be a national treasure of Nordic countries. These food products require zero resources to cultivate as they grow naturally in the forests. It is estimated that less than 10% of the total annual wild berry crop is harvested from the forests. The main challenge in collecting wild berries lies in the manual forest harvesting, namely pickers’ working conditions. Due to the short season, the majority of the work is conducted by foreigners with limited knowledge of the language, culture and forests. The FEROX project aims to utilize advances in AI, data, and robotics to improve the working conditions of the wild berry pickers. The project will employ autonomous drones equipped with various sensors to acquire data, build 3D models of the forests and, therefore, accurately estimate berries’ locations, amount and types. The collected data will be used to build AI models to help workers locate the berries and optimize their operations. In addition, FEROX will provide wild berry pickers with navigation and locating services and physical support to improve their working conditions and boost their trust and confidence. The holistic solution of FEROX will contribute to the overall safety of the workers by automatically monitoring the pickers and providing aid where it is needed. As a consequence, FEROX is expected to attract locals and hiking enthusiasts to work during summer in collecting wild products, hence, increasing the overall yield of the wild berries. These outcomes will open business opportunities for EU companies to adapt the solutions developed for industrialized cultivation as well as support global sustainability as technology providers for safe and sustainable berry harvesting. To demonstrate the solution, FEROX will conduct its tests in the forests in Finland with the support of the wild berries and mushroom ‘Arktiset Aromit’ (Arctic Flavours) Association.

Consortium (10)

Project Results (22)

Source: CORDIS, the EU research results database.

Publications (14)
Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions
Remote Sensing· 2025DOI
Jesse Muhojoki, Daniella Tavi, Eric Hyyppä, Matti Lehtomäki, Tamás Faitli, Harri Kaartinen, Antero Kukko, Teemu Hakala, Juha Hyyppä
Comparative Analysis of Ultra-Wideband and Mobile Laser Scanning Systems for Mapping Forest Trees under A Forest Canopy
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 2025DOI
Zuoya Liu, Harri Kaartinen, Teemu Hakala, Heikki Hyyti, Antero Kukko, Juha Hyyppa, Ruizhi Chen
FORMIGA: A Fleet Management Framework for Sustainable Human–Robot Collaboration in Field Robotics
Frontiers in Robotics and AI· 2025DOI
Yalcinkaya, B., Couceiro, M., Soares, S., & Valente, A.
Management of Unmanned Aerial Vehicles Operations in Forest Environments
2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)· 2025DOI
Marcos Terol, Francisco Fraile Gil, Andrés Boza, Pedro Gomez-Gasquet, Micael S. Couceiro
Optimizing Mapping Operations in Forest Environments: Technical and Usage Perspective
Lecture Notes on Data Engineering and Communications Technologies, Organizational Engineering, Coping with Complexity· 2025DOI
Marcos Terol, Francisco Fraile, Andrés Boza, Pedro Gomez-Gasquet
Use of Drones and AI for Wild Product Harvesting Optimization in the FEROX Project
2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)· 2025DOI
Laura Cristina Smith Ballester, Francisco Fraile Gil, Paul Chippendale, Micael Couceiro, Giacomo Piccinini
Comparing positioning accuracy of mobile laser scanning systems under a forest canopy
Science of Remote Sensing· 2024DOI
Jesse Muhojoki, Teemu Hakala, Antero Kukko, Harri Kaartinen, Juha Hyyppä
Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
2024 IEEE International Conference on Robotics and Automation (ICRA 2024)· 2024DOI
Beril Yalcinkaya; Micael S. Couceiro; Lucas Pina; Salviano Soares; António Valente; Fabio Remondino
Towards Robotization of Foraging Wild Fruits Under Canopy - A Multi-camera Drone-Borne Berry Mapping
Springer Proceedings in Advanced Robotics, European Robotics Forum 2024· 2024DOI
Paweł Trybała, Luca Morelli, Fabio Remondino, Micael S. Couceiro
Under-Canopy Drone 3D Surveys for Wild Fruit Hotspot Mapping
Drones· 2024DOI
Paweł Trybała, Luca Morelli, Fabio Remondino, Levi Farrand, Micael S. Couceiro
Unlocking the Potential of Human-Robot Synergy under Advanced Industrial Applications: The FEROX Simulator
European Robotics Forum 2024 (ERF 2024)· 2024DOI
Beril Yalcinkaya; André Araújo; Micael Couceiro; Salviano Soares; António Valente
Wild Berry image dataset collected in Finnish forests and peatlands using drones
European Conference on Computer Vision - ECCV Workshops· 2024DOI
Riz, L., Povoli, S., Caraffa, A., Boscaini, D., Mekhalfi, M. L., Chippendale, P., Turtiainen, M., Partanen, B., Smith Ballester, L., Noguera, F. B., Franchi, A., Castelli, E., Piccinini, G., Marchesotti, L., Santos Couceiro, M., Poiesi, F.
Developing unmanned aerial robotics to support wild berry harvesting in Finland: Human factors, standards and ethics.
ICRES 2023 Proceedings· 2023DOI
Fletcher, S., Oostveen, A.M., Chippendale, P., Couceiro, M., Turtiainen, M., Ballester, L.S.
Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
Sensors· 2023DOI
Beril Yalçinkaya, Micael Santos Couceiro, Salviano Pinto Soares, Antonio Valente
Deliverables (7)
Other Results (1)
Periodic Reporting for period 1 - FEROX (Fostering and Enabling AI, Data and Robotics Technologies for Supporting Human Workers in Harvesting Wild Food)