Review of machine learning techniques for mosquito control in urban environments

Abstract

Machine learning (ML) techniques excel at forecasting, clustering, and classification tasks, making them valuable for various aspects of mosquito control. In this literature review, we selected 120 papers relevant to the current state of ML for mosquito control in urban settings. The reviewed work covers several different methodologies, objectives, and evaluation criteria from various environmental contexts. We first divided the existing papers into geospatial, visual, or audio categories. For each category, we analyzed the machine learning pipeline, from dataset creation to model performance. We conclude with a discussion of the challenges and opportunities for further research. While the reviewed ML methods in mosquito control are promising, we recommend a) increased use of crowdsourced and citizen science data, b) a standardized and open ML pipeline for reproducible results, and c) research that incorporates advances in ML. With these suggestions, ML techniques could lead to effective mosquito control in urban environments. • This is the first literature review that considers a holistic ML approach to mosquito control in urban areas. • This review highlights geospatial, visual, and audio models for mosquito control from 120 selected papers. • We recommend researchers crowd-source data, use an open ML pipeline, and apply theoretical advancements in their work.