An alternative to the use of synthetic pesticides and antibiotics in agriculture is to spray local plants extracts, in aqueous or essential oil form. To this end, the Knomana knowledge base [1] compiles various knowledge sets on plant use such as the 42000 descriptions of pesticidal plant uses for plant, animal, and public health presented in the literature. As the One Health approach dictates to be aware of the additional uses of these pesticidal plants to prevent their unintended effects on the animal, the human, and their environment, the challenge for the domain experts (e.g. entomologist, pathologist) is thus to identify the pesticidal plants in Knomana considering the One Health approach. With the aim to present knowledge to the expert using a compact and comprehensive formalism, in [2], we computed the Duquenne-Guigues basis (DGB) of implications on an excerpt of Knomana, in which each plant is described using its taxonomy (i.e. species, genus, and family), its consumption as food, and its use in medical care. The DGB method is based on Formal Concept Analysis (FCA) and provides a cardinality-minimal set of non-redundant implications. By considering a reduced knowledge set, this work identified 3 types of knowledge elements in the implications: knowledge on plant use at diverse taxonomy levels (e.g. Plants from Meliaceae family are not consumed as food), plant taxonomy (e.g. A plant from Salvia genus is from Lamiaceae family), and side effect of the knowledge set (e.g. A plant from the Piperaceae family is from the genus Piper). This latter illustration is not in accordance with taxonomic referential and thus informs on the extent of knowledge inserted in Knomana. Moreover, as plant taxonomy is known by the experts, removing it from the implications eases their reading but makes it tacit knowledge. Implementing this method to select pesticidal plants requires to consider Knomana as a multidimensional (ternary) dataset, and thus to use the extension of FCA dev