Pest detection from a biology-informed inverse problem and pheromone sensors

One third of the annual world's crop production is directly or indirectly damaged by insects, with an even increasing burden in a warming climate. 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 locating the pest's habitat, before infestation. This early detection enables to perform pesticide-free elimination treatments and reduce the negative impact of agricultural practices on biodiversity, environment and human health, 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 inference of the pheromone emission (inverse problem) is performed. In the present case, classical inference framework consists in combining the data from the pheromone sensors and the fluid mechanic-based pheromone concentration dispersion model that is a 2D reaction-diffusion-convection model. The proposed inference framework further incorporates into this combination additional a priori biological knowledge on pest behaviour (favourite habitat, insect clustering for reproduction, population dynamic behaviour...) [1]. This information is introduced to constrain the inference problem towards biologically relevant solutions. Different biology-informed constraints are tested, and the accuracy of the solutions of the inverse problems is assessed on simulated noisy data using a dedicated package [2].  
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.

Reference:

[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.

Acknowledgements:

This work was carried out with the financial support of the French Research Agency through the Pherosensor project with grant agreement ANR-20-PCPA-0007.