Formal Concept Analysis (FCA) comes with a range of relevant techniques for knowledge analysis, such as conceptual structures or implications. The Duquenne-Guigues basis of implications provides a cardinality minimal set of non-redundant implications. The concern of a domain expert is to discover new knowledge within this implication set. The objective of this prospective paper is to collect and discuss the different patterns of implications extracted from a dataset on plants used in medical care or consumed as food. We identify 16 patterns combining 3 types of knowledge elements (KE). The patterns highlight redundant KEs, or KEs of little interest, in particular, those corresponding to plant taxonomy, as it is familiar knowledge for the experts. Removing these KEs from the implications would make them tacit. We suggest a postprocess for cleaning up the implications before reporting them to the experts. In addition, we discuss the different patterns and how an implication classification based on patterns could help the experts.