This study focuses on the critical investigation of the propagation of the Aedes aegypti mosquito, a vector responsible for transmitting various diseases. The significance lies in understanding its spread due to its potential to disseminate illnesses. Employing laboratory-engineered traps called IoT-Ovitraps, the research aims to construct maps illustrating egg deposition within a community. To achieve this, images featuring the objects of interest, namely Aedes aegypti eggs, are captured using a Raspberry Pi equipped with a micro lens. The primary objective centers on the detection and enumeration of Aedes aegypti eggs within the confines of Cauayan City. To ascertain the most effective methodology for achieving accurate egg quantification, the study employs three distinct models. These models are subsequently compared for their precision in estimating egg quantities present in the ovitraps. Among the models assessed, the convolutional neural network (CNN) emerges as the superior option in terms of efficiency and dependability. Remarkably, the CNN model attains an impressive accuracy rate of 99.5% in accurately detecting and enumerating Aedes aegypti eggs. This outcome underscores the potential of advanced machine learning techniques in contributing to effective disease vector monitoring and control strategies, highlighting the promising role of neural networks in tackling the challenges posed by disease-carrying mosquitoes.
