Biology-Informed inverse problems for insect pests detection using pheromone sensors
Most 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. They release pheromones in their nearby environment, volatile compounds that are detected by insects of the same species with exception...
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2025-02-01
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| author | Malou, Thibault Parisey, Nicolas Adamczyk-Chauvat, Katarzyna Vergu, Elisabeta Laroche, Béatrice Calatayud, Paul-André Lucas, Philippe Labarthe, Simon |
| author_facet | Malou, Thibault Parisey, Nicolas Adamczyk-Chauvat, Katarzyna Vergu, Elisabeta Laroche, Béatrice Calatayud, Paul-André Lucas, Philippe Labarthe, Simon |
| author_sort | Malou, Thibault |
| collection | DOAJ |
| description | Most 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. They release pheromones in their nearby environment, volatile compounds that are detected by insects of the same species with exceptional specificity and sensitivity. Efficient 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. In this paper, we develop a biology-informed inverse problem framework that leverages temporal signals from a pheromone sensor network to build insect presence maps. Prior biological knowledge is introduced in the inverse problem by the mean of a specific penalty, using population dynamics PDE residuals. We benchmark the biological-informed penalty with other regularization terms such as Tikhonov, LASSO or composite penalties in a simplified toy model. We use classical comparison criteria, such as target reconstruction error, or Jaccard distance on pest presence-absence. But we also use more task-specific criteria such as the number of informative sensors during inference. Finally, the inverse problem is solved in a realistic context of pest infestation in an agricultural landscape by the fall armyworm (Spodoptera frugiperda). |
| format | Article |
| id | doaj-art-05fdaca738e94689b70ad4b5767b90c6 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Peer Community In |
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| spelling | doaj-art-05fdaca738e94689b70ad4b5767b90c62025-02-07T10:34:51ZengPeer Community InPeer Community Journal2804-38712025-02-01510.24072/pcjournal.52010.24072/pcjournal.520Biology-Informed inverse problems for insect pests detection using pheromone sensors Malou, Thibault0https://orcid.org/0009-0002-3540-8789Parisey, Nicolas1https://orcid.org/0000-0003-2439-3809Adamczyk-Chauvat, Katarzyna2https://orcid.org/0000-0001-7953-9153Vergu, Elisabeta3Laroche, Béatrice4https://orcid.org/0000-0001-7821-332XCalatayud, Paul-André5https://orcid.org/0000-0002-9482-4646Lucas, Philippe6https://orcid.org/0000-0003-2166-8248Labarthe, Simon7https://orcid.org/0000-0002-5463-7256Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, FranceINRAE, Institute of Genetics, Environment and Plant Protection (IGEPP—Joint Research Unit 1349), Le Rheu, FranceUniversité Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, FranceUniversité Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, FranceUniversité Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France; Université Paris-Saclay, Inria, 91120, Palaiseau, FranceInstitut de Diversité, Ecologie et Evolution du Vivant (IDEEV), Université Paris-Saclay, CNRS, IRD, UMR Evolution, Génome, Comportement et Ecologie (EGCE), Gif-sur-Yvette, France; International Center of Insect Physiology and Ecology (icipe), Nairobi, KenyaInstitute of Ecology and Environmental Sciences of Paris (iEES-Paris - Joint Research Unit 1392 - INRAE, CNRS, IRD, Sorbonne Univ., UPEC, Univ. Paris Cité), INRAE, 78000, Versailles, FranceUniversité Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France; Univ. Bordeaux, INRAE, BIOGECO, 33610, Cestas, France; Univ. Bordeaux, Inria, INRAE, 33400, Talence, FranceMost 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. They release pheromones in their nearby environment, volatile compounds that are detected by insects of the same species with exceptional specificity and sensitivity. Efficient 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. In this paper, we develop a biology-informed inverse problem framework that leverages temporal signals from a pheromone sensor network to build insect presence maps. Prior biological knowledge is introduced in the inverse problem by the mean of a specific penalty, using population dynamics PDE residuals. We benchmark the biological-informed penalty with other regularization terms such as Tikhonov, LASSO or composite penalties in a simplified toy model. We use classical comparison criteria, such as target reconstruction error, or Jaccard distance on pest presence-absence. But we also use more task-specific criteria such as the number of informative sensors during inference. Finally, the inverse problem is solved in a realistic context of pest infestation in an agricultural landscape by the fall armyworm (Spodoptera frugiperda).https://peercommunityjournal.org/articles/10.24072/pcjournal.520/Inverse problemData assimilationBiology-informed estimationPDEPest detectionPheromone |
| spellingShingle | Malou, Thibault Parisey, Nicolas Adamczyk-Chauvat, Katarzyna Vergu, Elisabeta Laroche, Béatrice Calatayud, Paul-André Lucas, Philippe Labarthe, Simon Biology-Informed inverse problems for insect pests detection using pheromone sensors Peer Community Journal Inverse problem Data assimilation Biology-informed estimation PDE Pest detection Pheromone |
| title | Biology-Informed inverse problems for insect pests detection using pheromone sensors
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| title_full | Biology-Informed inverse problems for insect pests detection using pheromone sensors
|
| title_fullStr | Biology-Informed inverse problems for insect pests detection using pheromone sensors
|
| title_full_unstemmed | Biology-Informed inverse problems for insect pests detection using pheromone sensors
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| title_short | Biology-Informed inverse problems for insect pests detection using pheromone sensors
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| title_sort | biology informed inverse problems for insect pests detection using pheromone sensors |
| topic | Inverse problem Data assimilation Biology-informed estimation PDE Pest detection Pheromone |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.520/ |
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