Nathalie Peyrard | Searcharxiv

arXiv2020

Improving Pest Monitoring Networks in order to reduce pesticide use in agriculture

Disease and pest control largely rely on pesticides use and progress still remains to be made towards more sustainable practices. Pest Monitoring Networks (PMNs) can provide useful information for improving crop protection by restricting pesticide use to the situations that best require it. However, the efficacy of a PMN to control pests may depend on its spatial density and space/time sampling balance. Furthermore the best trade-off between the monitoring effort and the impact of the PMN information may be pest dependent. We developed a generic simulation model that links PMN information to treatment decisions and pest dynamics. We derived the number of treatments, the epidemic extension and the global gross margin for different families of pests. For soil-borne pathogens and weeds, we found that increasing the spatial density of a PMN significantly decreased the number of treatments (up to 67\%), with an only marginal increase in infection. Considering past observations had a second-order effect (up to a 13\% decrease). For the spatial scale of our study, the PMN information had practically no influence in the case of insects. The next step is to go beyond PMN analysis to design and chose among sustainable management strategies at the landscape scale.

q-bio.PE↗

arXiv2018

Exact and approximate inference in graphical models: variable elimination and beyond

Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, borrowed from optimisation and computer science. They are built on the principle of variable elimination whose complexity is dictated in an intricate way by the order in which variables are eliminated. The so-called treewidth of the graph characterises this algorithmic complexity: low-treewidth graphs can be processed efficiently. The first message that we illustrate is therefore the idea that for inference in graphical model, the number of variables is not the limiting factor, and it is worth checking for the treewidth before turning to approximate methods. We show how algorithms providing an upper bound of the treewidth can be exploited to derive a 'good' elimination order enabling to perform exact inference. The second message is that when the treewidth is too large, algorithms for approximate inference linked to the principle of variable elimination, such as loopy belief propagation and variational approaches, can lead to accurate results while being much less time consuming than Monte-Carlo approaches. We illustrate the techniques reviewed in this article on benchmarks of inference problems in genetic linkage analysis and computer vision, as well as on hidden variables restoration in coupled Hidden Markov Models.

stat.ML↗