In the framework of mapping underwater environments, Posidonia Oceanica (PO) is a fundamental indicator for environmental monitoring and related studies and analyses for climate change. The POSEIDON project aims to study, develop and test new methodologies to provide knowledge, as well as ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping and monitoring data for the conservation of the PO and related meadows and banquettes in the Mediterranean area. The project is funded by the National Recovery and Resilience Plan and stems from the recommendation of international organizations (European Environmental Directives, Mediterranean PO Network, International Partnership for Blue Carbon, Intergovernmental Oceanographic Commission of UNESCO in the Save the Wave project). Starting from new integrated survey methods and legacy data, the PO, ichthyofauna and seagrass banquettes will be mapped to understand their evolution and extract indicators and useful trends. Furthermore, the produced information will be stored and shared in a webGIS and a dedicated app to prevent unexpected damages during sailing or other activities. POSEIDON will use imaging sensors (RGB and multi/hyperspectral cameras) to extract information. With specific lighting equipment for significant water depths, these sensors will be conducted (by scuba divers or Autonomous Underwater Vehicles) next to the object to acquire BUHR data. Finally, to monitor the fauna (fish, other species, invertebrates), terrestrial and underwater videogrammetry will be applied using mono and stereo-cameras with object detection algorithms.

Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.

The increasing globalisation of trade and climate change are accelerating the spread of invasive pest species, posing significant threats to agriculture. The Japanese beetle (Popillia japonica Newman), first recorded in northern Italy in 2014, is a highly destructive pest with severe economic impacts, particularly in viticulture. Effective monitoring is essential for timely intervention, yet conventional field-based surveys are resource-intensive and limited in spatial coverage.This study presents a novel UAV-based monitoring framework integrating near-infrared (NIR) imaging and machine learning algorithms to detect Popillia japonica adults in vineyard environments. Field experiments were conducted in two commercial vineyards in northern Italy during the beetle's summer flight season. A standardised and replicable aerial data acquisition protocol was developed using lightweight multispectral sensors mounted on rotary-wing UAV platforms. Detected insect signatures were processed through a custom CV pipeline and validated through entomological ground truthing via manual counts.Results show a strong correlation between CV-derived detections and manual observations, with Pearson correlation coefficients ranging from 0.89 to 0.96. Although the system tends to overestimate insect counts under certain canopy conditions slightly, its integration into a GIS environment enabled the near real-time generation of prescription maps. These maps were used to guide site-specific drone spraying treatments, applying insecticides only in hotspot areas where infestation thresholds were exceeded.This UAV-enabled, semi-automated monitoring approach significantly reduces survey time and human exposure to agrochemicals, while supporting precision pest management at scale. The methodology offers a promising framework for integrating remote sensing, AI, and entomological validation, with broader applications for managing invasive species in precision agriculture contexts.

Posidonia oceanica (PO) meadows are important ecosystems in the Mediterranean, supporting biodiversity and ecosystem services, yet they face increasing threats due to anthropogenic pressures and climate change. To address these challenges, the POSEIDON project aims to develop ultra-high-resolution (UHR) and beyond ultra-high-resolution (BUHR) mapping methods for PO monitoring. This study focuses on underwater hyperspectral imagery (UHI) as a tool to complement and support the standard monitoring protocols established by ISPRA. Data were collected along the northern coast of Sardinia, with hyperspectral acquisitions performed both underwater, at depths of 5–15 meters, and out of the water, over collected samples, using a Senop Rikola hyperspectral camera. The study faced several challenges, including image blurring due to the movement of seagrass and light absorption by water, which limits the effectiveness of infrared bands. Despite these difficulties, the preliminary tests provided valuable insights into the system's limitations and suggested improvements for future data acquisition. This paper outlines the methodology, presents initial results, and discusses strategies to enhance underwater hyperspectral data quality for future monitoring efforts.