Thibault Malou (INRAE-MaIAGE)
One third of the annual world's crop production is directly or indirectly damaged by insects. Early detection of invasive insect pests is key for optimal treatment before infestation. Existing detection devices are based on pheromone traps: attracting pheromones are released to lure insects into the traps, with the number of captures indicating the population levels. Promising new sensors are on development to directly detect pheromones produced by the pests themselves and dispersed in the environment. Inferring the pheromone emission would allow deducing the pest population and locating the pest's habitat before infestation. This early and precise detection enables to perform pesticide-free elimination treatments, in a precision agriculture framework.
In order to identify the sources of pheromone emission from signals produced by sensors spatially positioned in the landscape, the pheromone emission inference is performed. In addition to the classical combination of data from pheromone sensors and a pheromone propagation model, we propose to improve the data assimilation by incorporating a priori biological knowledge on pest behaviour [1]. The biology-informed data assimilation method takes into account diverse biological information, including the favourite habitat, insect clustering for reproduction and, more importantly, the population dynamics of the pest.
This information is introduced in the costs function through dedicated regularization terms to constrain the inference towards biologically relevant pheromone emission, and especially toward pheromone emission that are consistent with population dynamics models. Different biology-informed, including population dynamic-informed, regularization terms are tested and the accuracy of the solutions of the inverse problems is assessed on simulated noisy data using a dedicated package [2]. This study highlights that taking into account a population dynamic model helps adress the issue of sparse spatio-temporal sampling of the data.
In addition, optimal experimental design will be presented to deduce optimal sensor position in order to reduce the uncertainty of the inference and to improve the prediction of pest's habitat localization.
[1] Malou T., Parisey N., Adamczyk-Chauvat K., Vergu E., Laroche B., Calatayud P.-A., Lucas P. and Labarthe S. (2024). Biology-Informed inverse problems for insect pests detection
using pheromone sensors. Submitted for publication. https://doi.org/10.5281/ZENODO.11506617
[2] Malou T. and Labarthe S. (2024). Pherosensor-toolbox: a Python package for Biology-Informed Data Assimilation. Journal of Open Source Software, 29 (101), 6863. https://doi.org/10.21105/joss.06863.
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