Trends and advances in image-based mosquito identification and classification using machine learning models: A systematic review

Mosquito-borne diseases, such as Yellow fever, Dengue, and Zika, pose a significant global health threat, causing millions of deaths annually. Traditional mosquito identification methods, reliant on expert analysis, are time-consuming and resource-intensive. Machine Learning (ML) has emerged as a transformative solution, enabling rapid and accurate species identification and classification. Recent studies leverage morphological features, such as wings and body structures, to determine species, sex, and age. These innovations aim to revolutionize vector control strategies, making them faster, more accurate, and widely accessible. This systematic review evaluates ML-based mosquito identification research, highlighting its strengths, limitations, and geographic disparities in contributions. Data was collected from Google Scholar, PubHub, IEEE Xplore, and ScienceDirect (2000–2024), with 52 studies meeting the inclusion criteria out of an initial pool of 1,050 papers. A key highlight of this review is the role of feature extraction techniques in achieving high classification accuracy by capturing fine-grained morphological traits. The findings also reveal critical limitations that hinder real-world applicability. These include limited dataset diversity, inconsistent preprocessing practices across devices, all of which reduce the generalizability of models in varied environments. Furthermore, high computational requirements and morphological similarities between certain species challenge the scalability and robustness of machine learning models. To address these gaps, measures such as expanding annotated and diverse datasets, investing in low-resource model deployment strategies, and supporting African-led research initiatives can be utilized to ensure more inclusive and context-relevant mosquito surveillance systems.