The project goes beyond existing forest pest monitoring practices by demonstrating how multiple digital tools can be effectively combined into an integrated early-warning approach.
New deep learning tools and a QGIS plugin for training deep learning models that further support species mapping and defoliation assessment were developed. Sentinel-1 and PALSAR-2 SAR data were processed to distinguish forest cover changes and identify causes. A multi-sensor monitoring strategy was drafted, combining Sentinel-1/2, PlanetScope, and UAV data for validation.
The optimisation of smart traps for bark beetles and processionary moths represents another significant improvement over standard monitoring techniques, with automated counting and real-time data access significantly reducing the workload and enabling faster responses to emerging threats. The technology is ready for use by the stakeholders and it will be demonstrated in the coming meeting.
The combined use of the Entomoscope imaging device and deep learning models enabled the accurate identification of several Agrilus species, and work is ongoing to extend this approach to more insect groups. The open-source nature of this technology, together with the relatively low cost of setting up and operating an Entomoscope, makes AI-based identification systems highly suitable for technology transfer initiatives. This approach could become a valuable tool for phytosanitary personnel, foresters, and environmental agencies, supporting more efficient monitoring and management of pest species.
To identify taxonomic and spatial gaps in citizen science data, a list of 1,781 native and non-native pest species was compiled and over 1.3 million iNaturalist observations were downloaded, with plans to integrate this dataset with records from GBIF. Preliminary results showed that search interest often peaked during or before outbreaks, indicating potential for early detection, but also highlighting challenges such as data context-dependence and lack of demographic detail.
Stakeholders were consulted to assess their needs, expectations, and constraints regarding the adoption of digital technologies for monitoring forest pests. The results revealed a strong alignment between the pest species prioritised by the FORSAID project and those considered important by stakeholders, underscoring the project's relevance and potential impact. Additionally, the survey provided insights into the digital tools currently employed by stakeholders, highlighting existing gaps and areas where improvements are needed to enhance detection, identification, and monitoring practices. Overall, the findings validated the strategic direction of FORSAID and provided valuable guidance for shaping the next steps of the project.
