Machine Learning-Driven Bird Nest Monitoring: Improving Performance and Innovations in Avian Conservation

Bird nest monitoring is a vital component of avian conservation because it provides substantial data on nesting habits, breeding success, and population dynamics. Traditional monitoring techniques rely on manual observations, which can be time-consuming and laborious and susceptible to errors made by humans. This paper's key objective is to shows how machine learning technology may be used to preserve bird nests and assess their health using images. The process of monitoring has several different parts, such as nest detection and activity tracking. A revolutionary method of avian conservation is provided by the integration of machine learning in bird nest monitoring. By utilizing the capability of automated data analysis, researchers can implement focused conservation methods, better understand the nesting behaviors of diverse bird species, and contribute to the preservation and protection of avian populations in an increasingly changing environment.