Back to the trees: Identifying plants with Human Intelligence

Abstract

We investigate a way to build a convivial plant identification tool halfway between the complex determination keys of botanists and the more recent but poorly explainable approaches based on AI image recognition. Our approach consists of a formal language to organize morphological traits and a Bayesian technique to describe plants with possible polymorphisms at all taxonomic levels, and to handle errors and uncertainties. From these structured data, automatic approaches can be designed to generate versatile determination keys , i.e. decision trees, which are otherwise tedious to design by hand.