Abstract
Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25–99.13% across 10 test videos and an average accuracy of 97.88%. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance.