The European Union's Biodiversity Strategy for 2030 emphasizes the need to halt biodiversity loss driven by anthropogenic activities. However, to achieve this, it is necessary to understand how those activities affect biodiversity so that evidence-based conservation interventions can be made. Therefore, effective monitoring tools are needed that can be used to survey biodiversity changes over large spatial and temporal scales. Conventional monitoring tools, based on expert field surveys, are time-consuming and expensive and, therefore, inappropriate for such large-scale monitoring. Consequently, researchers worldwide are increasingly turning their attention to new technologies. When it comes to monitoring vocalizing animals, such as birds, which have been shown to be appropriate indicators of biodiversity, researchers focus on the use of passive acoustic monitoring methods. However, further development of those methods is needed before conservation practitioners can use them widely and reliably in the field to monitor bird communities. BIOMON's main objective is to extend the current state-of-the-art acoustic monitoring approaches through the novel use of machine learning techniques. BIOMON's overarching goal is to develop an innovative passive acoustic monitoring protocol that can be used to survey bird communities in biodiverse agricultural farmlands in the EU. The EU is home to many species of which the persistence depends on low-intensity agricultural areas, and consequently, developing appropriate monitoring tools for those areas remains a key research priority. The methods and monitoring protocol developed in BIOMON, through the collaboration of the ER, a conservation biologist, and the supervisor, a machine learning expert, have the potential to contribute to the objectives of the EU's Biodiversity Strategy for 2030 and other related policies such as the Common Agricultural Policy.