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Insect pests are a major threat to agricultural systems [@oerkeCropLossesPests2006], leading to intensive use of pesticides for crop protection with non-sustainable drawbacks on environment, biodiversity and human health. Most insects produce pheromones for conspecific communication, making pheromone sensors a good tool for early specific detection of pests, in order to reduce pesticide use in a precision agriculture context [@gebbersPrecisionAgricultureFood2010].
Insect pests are a major threat to agricultural systems [@oerkeCropLossesPests2006], leading to intensive use of pesticides for crop protection with non-sustainable drawbacks on the environment, biodiversity, and human health. Most insects produce pheromones for conspecific communication, making pheromone sensors a good tool for early specific detection of pests, in order to reduce pesticide use in a precision agriculture context [@gebbersPrecisionAgricultureFood2010].
`Pheromone-toolbox` is a Python package containing numerical tools for pheromone sensor data assimilation to infer the position of emitting pest insects. It contains specific tools to model pheromone propagation and solve the corresponding inverse problem to determine emitters' position taking into account the environmental context (wind, landscape, vegetation...). A specific focus is put on the integration of biological knowledge of pest behavior during inference.
`Pheromone-toolbox` is a Python package containing numerical tools for pheromone sensor data assimilation to infer the position of emitting pest insects. It contains specific tools to model pheromone propagation and solve the corresponding inverse problem to determine emitters' position taking into account the environmental context (wind, landscape, vegetation...). A specific focus is put on the integration of biological knowledge of pest behavior during inference.
# Statement of need
# Statement of need
In the field of data assimilation for PDE and dynamic systems, existing packages provide tools to easily interface dynamic systems, observation models and a collection of data assimilation algorithms, such as DAPPER [@raanesDAPPERDataAssimilation2024], OpenDA [@ridlerDataAssimilationFramework2014] or PDAF [@nergerPDAFPARALLELDATA2005].
In the field of data assimilation for PDE and dynamic systems, existing packages provide tools to easily interface dynamic systems, observation models, and a collection of data assimilation algorithms, such as DAPPER [@raanesDAPPERDataAssimilation2024], OpenDA [@ridlerDataAssimilationFramework2014] or PDAF [@nergerPDAFPARALLELDATA2005].
Unlike these generic packages, `Pherosensor-toolbox` is a context-specific application-oriented package specifically designed to solve the inverse problem of inferring the source term (i.e. pheromone emitters position and emission rates) within a Chemical-Transport Model (CTM) modeling pheromone propagation in an agricultural landscape. An additional feature is the possibility of informing the data assimilation with insect behavior, such as the population dynamics partial differential equations (PDE), to get Biology-Informed Data-Assimilation (BI-DA). BI-DA aims to counter-balance data scarcity with prior biological knowledge. Another additional feature to come is optimal sensor placement tools to find the most informative sensor placement for assimilating the sensor data and inferring the source term of the CTM. The target audience is then academic researchers interested in epidemiosurveillance for crops.
Unlike these generic packages, `Pherosensor-toolbox` is a context-specific application-oriented package specifically designed to solve the inverse problem of inferring the source term (i.e. pheromone emitters position and emission rates) within a Chemical-Transport Model (CTM) modeling pheromone propagation in an agricultural landscape. An additional feature is the possibility of informing the data assimilation with insect behavior, such as the population dynamics partial differential equations (PDE), to get Biology-Informed Data-Assimilation (BI-DA). BI-DA aims to counter-balance data scarcity with prior biological knowledge. Another additional feature to come is optimal sensor placement tools to find the most informative sensor placement for assimilating the sensor data and inferring the source term of the CTM. The target audience is then academic researchers interested in epidemiosurveillance for crops.
