Evaluating formal threat criteria for every organism on earth is a tremendously resource-consuming task which will need many more years to accomplish at the actual rate. We propose here a method allowing for a faster and reproducible threat prediction for the 360,000+ known species of plants. Threat probabilities are estimated for each known plant species through the analysis of the data from the complete digitization of the largest herbarium in the world using machine learning algorithms, allowing for a major breakthrough in biodiversity conservation assessments worldwide. First, the full scientific names from Paris herbarium database were matched against all the names from the international plant list using a text mining open source search engine called Terrier. A series of statistics related to the accepted names of each plant were computed and served as predictors in a statistical learning algorithm with binary output. The training data was build based on the International Union for Conservation of Nature (IUCN) global Redlisting plants assessments. For each accepted name, the probability to be of least concern (LC, not threatened) was estimated with a confidence interval and a global misclassification rate of 20%. Results are presented on the world map and according to different plant traits.