As part of the ecological transition, climate shifts and human-induced changes stress global vegetation. Pollen is a key component in ecosystem maintenance. Monitoring it is then essential for analysing climate impact, biodiversity preservation, ecosystem health, sustainable agriculture, allergy diagnosis, and early warnings. Developing accurate airborne pollen monitoring sensors, assessing pollen diversity, abundance and vertical distribution, is then crucial. In this context, the APRIL project aims to contribute to this work by developing a cutting-edge pollen remote sensing instrument, identifying pollen types as a function of altitude, in real-time, without the need for sampling, under atmospheric conditions. The APRIL biosensor also evaluates pollen levels, displayed in a color-coded format, akin to atmospheric pollution, for warning diagnosis. To reach these objectives, a transdisciplinary consortium has been formed among INP (iLM), INSU (Pytheas), and a non-lucrative private partner (RNSA). Their combined expertise includes lidar remote sensing, polarimetry, spectroscopy, meteorology, atmospheric physics, aerobiology, dispersion models. The APRIL instrument indeed identifies pollen types remotely, using lidar field observations and a laboratory database of pollen optical fingerprints, using artificial intelligence for automated pollen recognition from lidar data. As part of the APRIL methodology, this laboratory database is expanded to account for wild airborne pollen under in-situ humidity conditions. The APRIL sensor is validated at the Observatoire de Haute Provence OHP, considered as a rural site, compared with standard pollen counters at various heights above ground. At the end of the project, APRIL will be measuring pollen types, quantities and vertical distribution over two seasons. This lidar data is planned to be used as input for 3D dispersion modelling, to improve pollen risk forecasting.