The overall objective of the PoC-DRONE4TREE project is to conduct a proof of concept (PoC) of autonomous drone-in-a-box (DiaB) based remote sensing technology for single-tree forestry applications. The project aims to overcome the present bottleneck in drone operations, namely the visual line of sight (VLOS) onsite operation by an autonomous DiaB solution. In this novel approach, a drone is hosted by a box which takes care autonomously on drone takeoff and landing, as well as on charging and data transfer operations. On specified triggering events, the drone will start a data collection task. After task completion, the drone returns to the box, which triggers the parallel operations of battery charging and data transfer to a processing unit (e.g. in cloud or on local computer). Together with project collaborators, operational concepts will be designed for two connected applications: the tree health analysis and selective harvesting decision support. The system is implemented as a remotely operated application, where all phases of the process will be guided via Internet using cloud-based application, including the interactive mission planning, autonomous execution of flights, AI based data processing and information extraction, as well as sharing of the analysis results to the end users. 

The project follows a quadruple helix approach of interaction. User requirements surveys cover widely the UNITE stakeholder community and beyond (industry, society, science), while the actual applications and demonstrations will be implemented in close interaction with two core industrial collaborators of the project, the Afry X and Motoajo Oy. An agile approach is used in implementation, meaning that several iterative plan-develop-demonstrate-assess cycles will be carried out. Throughout the process, the stakeholders will be engaged to follow and co-innovate the system. Results will be communicated to the core collaborators as well as more widely to industry, society, public, and science. Evaluation of exploitation possibilities of the new method are also vital part of the project. By using novel innovative ways to collect forestry data and carrying out single tree analysis using the point cloud and multispectral remote sensing datasets, the project capitalizes the core research findings of the UNITE flagship, and facilitates in a strong way the commercial utilization of the UNITE top science.