Pest localization through sequential data assimilation with pheromone sensors

Early detection and precise localization of insect pests are critical for the development of effective crop management strategies. This study introduces a Sequential Data Assimilation (SDA) method that integrates a pheromone propagation model with data pheromone sensors to infer spatio-temporal pheromone emission maps and deduce insect localization.The SDA method iteratively refines sensor positioning by leveraging predicted pheromone plumes, enabling sensors to adaptively reposition toward areas of higher pheromone concentration. This approach combines data assimilation, large-scale physics modeling, and statistical optimization to improve localization accuracy over time.The method is evaluated through numerical experiments, including toy cases with unsteady wind conditions and multiple pheromone sources, as well as a realistic case based on real agricultural landscapes and meteorological data. Results demonstrate that the method significantly enhances the accuracy of pest localization compared to traditional data assimilation approaches. The sequential repositioning of sensors reduces errors in pheromone emission inference and minimizes false absence predictions, even in challenging scenarios such as unsteady wind or multiple emission sources. This study highlights the potential of SDA for robust pest localization, offering a promising tool for precision agriculture and sustainable pest management.