@article{fdi:010092817, title = {{B}iology-{I}nformed inverse problems for insect pests detection using pheromone sensors}, author = {{M}alou, {T}. and {P}arisey, {N}. and {A}damczyk-{C}hauvat, {K}. and {V}ergu, {E}. and {L}aroche, {B}. and {C}alatayud, {P}aul-{A}ndr{\'e} and {L}ucas, {P}. and {L}abarthe, {S}.}, editor = {}, language = {{ENG}}, abstract = {{M}ost insects have the ability to modify the odor landscape in order to communicate with their conspecies during key phases of their life cycle such as reproduction. {T}hey release pheromones in their nearby environment, volatile compounds that are detected by insects of the same species with exceptional specificity and sensitivity. {E}fficient pheromone detection is then an interesting lever for insect pest management in a precision agroecological culture context. {A} precise and early detection of pests using pheromone sensors offers a strategy for pest management before infestation. {I}n this paper, we develop a biology- informed inverse problem framework that leverages temporal signals from a pheromone sensor network to build insect presence maps. {P}rior biological knowledge is introduced in the inverse problem by the mean of a specific penalty, using population dynamics {PDE} residuals. {W}e benchmark the biological-informed penalty with other regularization terms such as {T}ikhonov, {LASSO} or composite penalties in a simplified toy model. {W}e use classical comparison criteria, such as target reconstruction error, or {J}accard distance on pest presence-absence. {B}ut we also use more task-specific criteria such as the number of informative sensors during inference. {F}inally, the inverse problem is solved in a realistic context of pest infestation in an agricultural landscape by the fall armyworm ({S}podoptera frugiperda).}, keywords = {}, booktitle = {}, journal = {{P}eer {C}ommunity {J}ournal}, volume = {5}, numero = {}, pages = {e19 [60 ]}, ISSN = {2804-3871}, year = {2025}, DOI = {10.24072/pcjournal.520}, URL = {https://www.documentation.ird.fr/hor/fdi:010092817}, }