Cyanobacteria blooms frequently disturb the functioning of freshwater ecosystems and their uses, due to the toxins dangerous to health that cyanobacteria are able to synthesize. Therefore, many countries have implemented monitoring programs aimed at reducing the risk of human exposure to these toxins. The main limitation is related to the heterogeneity of the spatial distribution of cyanobacteria. In the vertical dimension, these organisms can stay in different layers in the water column and in the horizontal scale, the cells may accumulate in some a rea of the water body, under the action of winds or currents. In an attempt to improve monitoring, many research projects have been undertaken in order to develop new tools, like buoys equipped with various underwater sensors. This tool is highly relevant but it does not allow assessing the horizontal distribution of cyanobacteria and its cost remains expensive. Moreover, if satellite remote sensing can be considered very useful for estimating biomass and horizontal distribution of cyanobacteria in a water body, the cost of this technology and the lack of satellite availability make it unaffordable for routine monitoring. In this context, our project named OSS-CYANO (Development of optical sensors and drone system for the survey of cyanobacteria) project aims in a first time to develop and validate a new, low-cost aerial sensor. In this goal, we developed a framework for performing high-throughput measurements of the absorbance and reflectance of small volumes of controlled mixtures of phytoplankton species and studied the potential of this framework to validate remote-sensing proxies of cyanobacteria concentration. A systematic test of every possible two-band and three-band normalized difference between optical indices was then performed, and the coincidental correlation with chlorophyll-b (absent in cyanobacteria) was used as an indicator of non-specificity. Two-band indices were shown to suffer from non-specificity issues and could not yield strong and specific relationships with phycocyanin or phycoerythrin (maximum R² 0.8). The second part of our program aimed to couple the high-frequency measurements and a physically-based three-dimensional hydro-ecological model for predicting cyanobacteria blooms. Finally, the third part of our program aimed to implement a drone capable of carrying the sensor to perform spatial measurements on large water bodies, and other instruments for water sampling or for performing underwater measurements. Six public research laboratories and one private company have been involved in this project.