Model-Based Estimation and Control of Agricultural Infestations Through Abiotic Changes

ended

The goal of this project is to contribute to the ongoing “precision agriculture” revolution by developing model-based control strategies for pest control. The project will be carried out with a high interdisciplinary approach between control and system engineers and entomologists. We firstly aim to develop models and methodologies that can guide in an optimal way the agronomical decisions (both in terms of monitoring and of control actions) that impact pest control. To pursue this aim, the first part of the project will be devoted to the development and validation of models able to capture both the spatial diffusion and the temporal evolution of insects in a plantation. These models will be parametrized w.r.t. environmental parameters (temperature and humidity) and control inputs (e.g., pesticides). The focus will be then shift on how to use these models for pest monitoring, and in particular on the interplay between quality of the estimation and data collection, in order to develop optimal data collection policies. In the last part of the project, we will use the developed results to develop model-based optimal pest control policies. Although we aim to obtain a theoretical framework that is more general as possible, two species of high agricultural interest (Drosophila suzukii and Halyomorpha halys) will be considered to validate the various steps of the theory. This research project has a highly innovative approach and has the ambition at large to promote a new philosophy on pest management that goes beyond qualitative analysis and decision making, proposing a quantitative model-based approach to pest management. The research objectives are highly original and focus on aspects of pest management that are highly relevant both from the applied and the research viewpoint. We believe that the research and the tools that will be developed in this research project will have a major impact on both the scientific and the farmers community.