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 heromones 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, 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. Classical inference is conducted by combining the data and the so-called direct model [1]. In the present case, this entails combining the data from the pheromone sensors and the pheromone concentration dispersion that is a 2D reaction-diffusion-convection model [2]. In the proposed method, the inference involves not only the coupling of the pheromone dispersion model with the pheromone sensors data but also incorporates a priori biological knowledge on pest behaviour (favourite habitat, insect clustering for reproduction, population dynamic behaviour...). This information is introduced to constrain the inverse 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.[1] Bocquet, M. (2014). Introduction to the Principles and Methods of Data Assimilation in the Geosciences. Lectures note.[2] Stockie, J.M. (2011). The Mathematics of Atmospheric Dispersion Modeling. SIAM Review.< Réduire