A STAND-ALONE TOOL FOR MOSQUITO EGG ENUMERATION

Accurate enumeration of mosquito eggs is crucial for various entomologic studies, including investigations into mosquito fecundity, life history traits, and vector control strategies. Traditional manual counting methods are labor intensive and prone to human error, highlighting the need for automated systems. This study presents a stand-alone automated mosquito egg counting system using a Raspberry Pi computer, high-quality camera, light-emitting diode ring light source, and a Python script leveraging the Open Source Computer Vision library. Linear regression analysis comparing automated and manual counts yielded a slope of 1.009 and an R2 value of 0.999, indicating a strong correlation between the methods. Bland-Altman analysis showed a bias of -0.5, with 95% limits of agreement ranging from -11.88 to 10.88. These results demonstrate the high accuracy and reliability of this system in laboratory settings. The automated system's portability, cost-effectiveness, and independence from an external computer make it particularly useful for diverse research environments. Variability in egg size and potential inaccuracies in field conditions with multiple mosquito species highlight areas for further refinement, and future work will focus on optimizing the counting algorithm and validating its performance across different mosquito species and rearing conditions to enhance its applicability in vector research.