MATERIALS AND METHODS: The authors conducted a literature search in four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) and completed it in December 2022. Articles that do not directly address the application of drones for surveillance and control of mosquito breeding sites were excluded.
RESULTS: The initial search using the keywords yielded 623 documents. After screening abstracts and reviewing the full text, only 17 articles met the inclusion criteria. Most of the studies were in the proof-of-concept stage. Many studies have also incorporated drone technologies and machine learning techniques into surveillance efforts. The authors have highlighted seven key issues related to the operational aspects of using drones. Those are hardware, software, law and regulation, operating time, expertise, geography, and community involvement.
CONCLUSION: With rapid developments in drone technologies and machine learning techniques, the viability of drones as surveillance tools can be enhanced, thus effectively responding to global public health concerns.
RESULTS: Our results show that using the MobileNetV2 architecture with transparent sticky traps as training data, the model predicted the pest species on transparent sticky traps with an accuracy of at least 0.95 and on other sticky trap colours with at least 0.85 of the F1 score. Using a generalised linear model (GLM) and a Boruta feature selection algorithm, we also showed that the colour and architecture of the sticky traps significantly influenced the performance of the model.
CONCLUSION: Our results support the development of an automatic classification of pests on a sticky trap, which should focus on colour and deep learning architecture to achieve good results. Future studies could aim to incorporate the trap system into pest monitoring, providing more accurate and cost-effective results in a pest management programme. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.